System and method for physiological parameter monitoring

ABSTRACT

The present disclosure relates to a device, method and system for calculating, estimating, or monitoring the physiological parameters of a subject. At least one processor, when executing instructions, may perform one or more of the following operations. A first signal representing a pulse wave relating to heart activity of a subject may be received. A plurality of second signals representing time-varying information on the pulse wave may be received. A blood oxygen level of the subject based on the plurality of second signals may be determined. A first feature in the first signal may be identified. A second feature in one of the plurality of second signals may be identified. A pulse transit time based on a difference between the first feature and the second feature may be computed. A blood pressure of the subject may be calculated based on the pulse transit time.

CROSS-REFERENCE TO RELATED APPLICATIONS

The application is a continuation of U.S. application Ser. No.15/741,278 filed on Dec. 31, 2017, which is a 371 of InternationalApplication No. PCT/CN2016/077469 filed Mar. 28, 2016, which claimspriority to International Application No. PCT/CN2015/083334 filed Jul.3, 2015, International Application No. PCT/CN2015/096498 filed Dec. 5,2015, and International Application No. PCT/CN2016/070017 filed Jan. 4,2016, each of which is hereby incorporated by reference.

TECHNICAL FIELD

The present disclosure generally relates to a system and methodapplicable in health-care related areas. More particularly, the presentdisclosure relates to a system and method for physiological parametermonitoring.

BACKGROUND

A traditional blood pressure measurement system, also calledsphygmomanometers, employs Korotkoff sounds or an oscillometric methodto determine blood pressure based on the relationship of the externalpressure and magnitude of arterial volume pulsations. A traditionalblood oxygen measurement system uses photoelectric sensors placed on thefinger of a patient to detect pulse wave related signals to evaluate ablood oxygen level. Such systems and methods work separately and usuallydiscontinuously with an interval of a few minutes or longer betweenconsecutive measurements. Continuous monitoring of multiplephysiological parameters may be beneficial for, for example,hypertension management and cardiovascular risk prediction.

SUMMARY

Some embodiments of the present disclosure relates to a device includingmemory storing instructions, and at least one processor. The device mayestimate or monitor the physiological parameters of a subject. When theat least one processor executing the instructions, the at least oneprocess may perform one or more of the following operations. A firstsignal representing a pulse wave relating to heart activity of a subjectmay be received. A plurality of second signals representing time-varyinginformation on the pulse wave may be received. A blood oxygen level ofthe subject based on the plurality of second signals may be determined.A first feature in the first signal may be identified. A second featurein one of the plurality of second signals may be identified. A pulsetransit time based on a difference between the first feature and thesecond feature may be computed. A blood pressure of the subject may becalculated based on the pulse transit time.

Some embodiments of the present disclosure relates to a methodimplemented on at least one processor for estimating or monitoring thephysiological parameters of a subject. The method may include one ormore of the following operations. A first signal representing a pulsewave relating to heart activity of a subject may be acquired. Aplurality of second signals representing time-varying information on thepulse wave may be acquired. A blood oxygen level of the subject based onthe plurality of second signals may be determined. A first feature inthe first signal may be identified. A second feature in one of theplurality of second signals may be identified. A pulse transit timebased on a difference between the first feature and the second featuremay be computed. A blood pressure of the subject may be calculated basedon the pulse transit time.

Some embodiments of the present disclosure relates to a systemimplemented on memory and at least one processor. The system may be usedto estimate or monitor the physiological parameters of a subject. Thesystem may include a first acquisition module, a second acquisitionmodule, a calibration unit and an analysis module. The first acquisitionmodule may acquire a first signal representing heart activity of asubject. The second acquisition module may acquire a plurality of secondsignals representing time-varying information on the pulse wave, anddetermine a blood oxygen level of the subject based on the plurality ofsecond signals. The calibration unit may acquire a set of calibrationdata. The analysis module may identify a first feature in the firstsignal; identify a second feature in one of the plurality of secondsignals; compute a pulse transit time based on a difference between thefirst feature and the second feature, and calculate a blood pressure ofthe subject based on the pulse transit time.

In some embodiments, the receiving the first signal may includecommunicating with a first sensor that may acquire the first signal ofthe subject. Receiving the first signal may include measuring oracquiring the first signal using a first sensor that may acquire thefirst signal. The first sensor may be part of the device. The receivingthe plurality of second signals may include communicating with one ormore second sensors. Receiving the plurality of second signals mayinclude measuring or acquiring the second signals using one or moresecond sensors. The one or more second sensors may be part of thedevice. The first sensor may include a plurality of electrodes. The oneof the one or more second sensors may include a photoelectric sensor.

In some embodiments, the first signal or the second signal may includean optical signal or an electric signal. The first signal or the secondsignal may include a photoplethysmography (PPG) waveform, anelectrocardiography (ECG) waveform, or a ballistocardiogram (BCG)waveform.

In some embodiments, the first feature of the first signal maycorrespond to a first time point. The identifying the second feature mayinclude selecting a segment of the second signal, the segment occurringwithin a time window from the first time point; and locating the secondfeature corresponding to a second time point in the segment. Thecomputing the pulse transit time may include determining a time intervalbetween the first time point and the second time point.

In some embodiments, the at least one processor may further include orcommunicate with a cuff-based blood pressure monitor. The cuff-basedblood pressure monitor may coordinate the blood pressure measurementwith the first signal and the plurality of second signals.

In some embodiments, the at least one processor may further receiveinformation relating to the subject or a condition when the first signalor the second signal is acquired. Exemplary information may include,e.g., age, body weight, the time (during the day) or the date the firstsignal or the second signal is acquired, the room temperature, the moodof the subject at the time, whether the subject has recently exercised,or the like, or a combination thereof. Such information may be takeninto consideration when the blood pressure of the subject is calculatedusing the device.

Additional features will be set forth in part in the description whichfollows, and in part will become apparent to those skilled in the artupon examination of the following and the accompanying drawings or maybe learned by production or operation of the examples. The features ofthe present disclosure may be realized and attained by practice or useof various aspects of the methodologies, instrumentalities andcombinations set forth in the detailed examples discussed below.

BRIEF DESCRIPTION OF THE DRAWINGS

The present disclosure is further described in terms of exemplaryembodiments. These exemplary embodiments are described in detail withreference to the drawings. These embodiments are non-limiting exemplaryembodiments, in which like reference numerals represent similarstructures throughout the several views of the drawings, and wherein:

FIG. 1 illustrates an exemplary system configuration in which a systemfor monitoring a physiological signal may be deployed in accordance withvarious embodiments of the present disclosure;

FIG. 2 depicts an exemplary diagram of an engine of the systemillustrated in FIG. 1, according to some embodiments of the presentdisclosure;

FIG. 3 is a flowchart of an exemplary process in which a method forestimating a physiological signal is deployed, according to someembodiments of the present disclosure;

FIG. 4 is a block diagram illustrating an architecture of an acquisitionmodule according to some embodiments of the present disclosure;

FIG. 5 is a block diagram illustrating an architecture of a secondacquisition unit according to some embodiments of the presentdisclosure;

FIG. 6 is a block diagram illustrating an architecture of an analysismodule according to some embodiments of the present disclosure;

FIG. 7 is a block diagram illustrating an architecture of a calibrationunit according to some embodiments of the present disclosure;

FIG. 8 is a flowchart of a process for estimating a physiologicalparameter of interest according to some embodiments of the presentdisclosure;

FIG. 9-A and FIG. 9-B provide exemplary processing regarding aphysiological parameter monitoring according to some embodiments of thepresent disclosure;

FIG. 10 illustrates an exemplary personal health manager according tosome embodiments of the present disclosure;

FIG. 11 depicts the architecture of a mobile device that may be used toimplement a specialized system or a part thereof incorporating thepresent disclosure;

FIG. 12 depicts the architecture of a computer that may be used toimplement a specialized system or a part thereof incorporating thepresent disclosure; and

FIG. 13 illustrates an exemplary device according to some embodiments ofthe present disclosure.

DETAILED DESCRIPTION

In the following detailed description, numerous specific details are setforth by way of examples in order to provide a thorough understanding ofthe relevant disclosure. However, it should be apparent to those skilledin the art that the present disclosure may be practiced without suchdetails. In other instances, well known methods, procedures, systems,components, and/or circuitry have been described at a relativelyhigh-level, without detail, in order to avoid unnecessarily obscuringaspects of the present disclosure.

The present disclosure relates to system, method, and programmingaspects of physiological parameter monitoring, for example, bloodpressure monitoring. The system and method involve improved sensordesign and signal processing. The system and method as disclosed hereinmay monitor multiple physiological parameters. The characteristics ofthe system and method may include, for example, real time, simultaneity,continuity, non-invasiveness, improved accuracy, or the like, or acombination thereof. In some embodiments, the system and method asdisclosed herein may monitorvarious cardiovascular activities andrelated information including, for example, blood pressure information,ECG information, blood oxygenation information, or the like, or acombination thereof. In some embodiments, a blood pressure may beestimated based on pulse wave related information, for example, pulsetransit time (PTT), pulse arrival time (PAT), or the like, or acombination thereof. In some embodiments, a blood oxygen level may beestimated based on photoplethysmogram (PPG) signals. The system andmethod as disclosed herein may be used in a healthcare institute (e.g.,a hospital) or at home. The following description is provided withreference to PTT in connection with the blood pressure monitoring forillustration purposes, and is not intended to limit the scope of thepresent disclosure. Merely by way of example, the system and method asdisclosed herein may utilize one or more other pulse wave relatedinformation or signals, for example, PAT, for blood pressure monitoring.

These and other features, and characteristics of the present disclosure,as well as the methods of operation and functions of the relatedelements of structure and the combination of parts and economies ofmanufacture, may become more apparent upon consideration of thefollowing description with reference to the accompanying drawing(s), allof which form a part of this specification. It is to be expresslyunderstood, however, that the drawing(s) are for the purposes ofillustration and description only and are not intended to limit thescope of the present disclosure. As used in the specification and in theclaims, the singular form of “a,” “an,” and “the” include pluralreferents unless the context clearly dictates otherwise.

FIG. 1 illustrates an exemplary system configuration in which a system100 may be deployed in accordance with some embodiments of the presentdisclosure. The system 100 may monitor a physiological parameter ofinterest. The system 100 may include a measuring device 110, a database(for example, a server 120), an external data source 130, and a terminal140. Various components of the system 100 may be connected to each otherdirectly or indirectly via a network 150.

The measuring device 110 may measure a signal. The signal may be acardiovascular signal. The signal may relate to or be used to calculateor estimate a physiological parameter of interest. The measuring device110 may include, for example, a clinical device, a household device, aportable device, a wearable device, or the like, or a combinationthereof. As used herein, a clinical device may be one that meetsapplicable standards and/or specifications to be used in a clinicalsetting including, for example, a hospital, a doctor's office, a nursinghome, or the like. A clinical device may be used by or with theassistance of a healthcare provider. As used herein, a household devicemay be one that meets applicable standards and/or specifications to beused at home or a nonclinical setting. A household device may be used bysomeone who is or is not a professional provider. A clinical device or ahousehold device, or a portion thereof, may be portable or wearable.Exemplary clinical devices include an auscultatory device, anoscillometric device, an ECG monitor, a PPG monitor, or the like, or acombination thereof. Exemplary household devices include anoscillometric device, a household ECG monitor, a sphygmometer, or thelike, or a combination thereof. Exemplary portal devices include anoscillometric device, a portable ECG monitor, a portable PPG monitor, orthe like, or a combination thereof. Exemplary wearable devices include apair of glasses 111, a shoulder strap 112, a smart watch 113, an anklet114, a thigh band 115, an armband 116, a chest belt 117, a necklet 118,or the like, or a combination thereof. The above mentioned examples ofmeasuring devices 110 are provided for illustration purposes, and notintended to limit the scope of the present disclosure. A measuringdevice 110 may be in another form including, for example, a fingerstall,a wristband, a brassiere, an underwear, a chest band, or the like, or acombination thereof.

Merely byway of example, the measuring device 110 is a wearable orportable device that may measure one or more cardiovascular signals. Insome embodiments, the wearable or portable device may process at leastsome of the measured signals, estimate a physiological parameter ofinterest based on the measured signals, display a result including thephysiological parameter of interest in the form of, for example, animage, an audio alert, perform wired or wireless communication withanother device or server (for example, the server 120), or the like, ora combination thereof. In some embodiments, the wearable or portabledevice may communicate with another device (for example, the terminal140) or a server (for example, a cloud server). The device or server mayprocess at least some of the measured signals, estimate a physiologicalparameter of interest based on the measured signals, display a resultincluding the physiological parameter of interest in the form of, forexample, an image, an audio alert, or the like, or a combinationthereof.

In some embodiments, the operations of processing the measured signals,estimating a physiological parameter, displaying a result, or performingwired or wireless communication may be performed by an integrated deviceor by separate devices connected to or communicating with each other.Such an integrated device may be portable or wearable. In someembodiments, at least some of the separate devices may be portable orwearable, or located in the vicinity of a subject whose signal ismeasured or a physiological parameter of interest is estimated ormonitored. Merely by way of example, the subject wears the measuringdevice 110 that may measure one or more cardiovascular signals; themeasured one or more cardiovascular signals are transmitted to a smartphone that may calculate or estimate a physiological parameter ofinterest based on the measured signals. In some embodiments, at leastsome of the separate devices are located in a location remote from thesubject. Merely by way of example, the subject wears the measuringdevice 110 that may measure one or more signals; the measured one ormore signals are transmitted to a processor that may calculate orestimate multiple physiological parameters of interest based on themeasured signals; the calculated or estimated physiological parametersof interest may be provided to the subject, or a user other than thesubject (for example, a doctor, a care provider, a family memberrelating to the subject, or the like, or a combination thereof).

In some embodiments, the measuring devices 110 may include various typesof sensors including, for example, an electrode sensor, an opticalsensor, a photoelectric sensor, a pressure sensor, an accelerometer, agravity sensor, a temperature sensor, a moisture sensor, or the like, ora combination thereof. The measuring device may monitor and/or detectone or more types of variables including, for example, temperature,humidity, user or subject input, or the like, or a combination thereof.The measuring devices 110 may also include a positioning system, forexample, a GPS receiver, or a location sensor, and the positioninformation may be transmitted to the server 120, the external datasource 130, the terminal 140, or the like, or a combination thereof,through the network 150. The position information and measured signalsmay be transmitted simultaneously or successively.

The system may include or communicate with a server or a databaseconfigured for storing a library 1100 and/or algorithms 121. The serveror database may be the server 120. The server 120 may be a cloud server.Merely by way of example, the server 120 may be implemented in a cloudserver that may provide storage capacity, computation capacity, or thelike, or a combination thereof. The library 1100 may collect or storedata. The data may include personal data, non-personal data, or both.The data may include static data, dynamic data, or both. Exemplarystatic data may include various information regarding a subjectincluding identity, contact information, birthday, a health history (forexample, whether a subject has a history of smoking, informationregarding a prior surgery, a food allergy, a drug allergy, a medicaltreatment history, a history of genetic disease, a family healthhistory, or the like, or a combination thereof), the gender, thenationality, the height, the weight, the occupation, a habit (forexample, a health-related habit such as an exercise habit), theeducation background, a hobby, the marital status, religious belief, orthe like, or a combination thereof. Exemplary dynamic data may include acurrent health condition of a subject, medications the subject istaking, a medical treatment the subject is undertaking, diet,physiological signals or parameters (for example, pulse transit time(PTT), systolic blood pressure (SBP), diastolic blood pressure (DBP), orthe like) relating to the subject for multiple time points or over aperiod of time, or the like, or a combination thereof.

The library 1100 maybe stored locally on a measuring device 110, or aterminal 140. The library 1100 may include different sections (e.g., apersonal data, a universal data, or the like) with different accesscontrol level. For example, personal data may record data andinformation associated with each individual users, but a subject mayhave different access permits to different parts of personal data. Forexample, Subject 1's personal data, Subject 2's personal data, andSubject N's personal data may be stored in the library 1100, but Subject1 may only have full access to his/her personal data and limited accessto other user's personal data.

The personal data may further include, but not limited to, headers,histories, and preferences. Additionally, a header may have a subject'sbasic information and medical records. A header may include, but notlimited to, subject's age, gender, race, occupation, health condition,medical history, life style, marital status, and other personalinformation. A history may record measured data (M), calibration values(C) (or calibration data), results (SBP, DBP, BP) and additionalinformation associated with every measurement and/or calibration.Furthermore, additional information may be any internal or externalvariables occurred when a subject is conducting a measurement and/orcalibration. External variables may include, room temperature, humidity,air pressure, weather, climate, time, and date, etc. Internal variablessuch as, body temperature, metabolism rate, mood, level of activity,type of activity, diet, and health condition, etc. The above mentionedexamples of additional information are only to provide a betterillustration, additional information associated with each measurementand/or calibration may be other types of information, such as viscosityand other rheological data of a subject's blood. In some embodiments,the concepts of additional information and information recorded in aheader are interchangeable. When some information originally recorded ina header changes with each measurements, it may also be considered asadditional information.

Preference may have information associated with models, for example, asubject's favorite models and coefficients, and favorite modelsapplicability, indicating which favorite model(s) are used under whatkind of conditions or with what additional information. A subject'shistorical data may refer to all the information stored under a history.Preference may also include a rating of a subject, which rates thereliability of the subject's personal data and may be considered as aweight factor when sorting the subject's personal data into peer data.For example, a subject who uploads calibration values (C) every week mayhave a better rating as compared to another subject who only calibratesonce every year. The above mentioned examples of information recorded ina preference, and a preference may include other information, such aswhich part of personal data a subject is willing to share with otherusers or organizations.

The universal data may include some non-private or non-personalizeddata, which may be accessed by other users or subjects. The universaldata may include the records of the database of all the models, logics,and public data, for example, models and coefficients, logical judgmentsto sort peer data from personal data, and statistical results related tocalibration values. Peer data may be sorted from multiple subjects'personal data, and logical judgments to sort peer data from personaldata serve to find most closely related data according the subjects'headers, and additional information in histories. Logical judgments tosort peer data from personal data may also consider ratings inpreferences to weigh the data acquired from different subjects. Theabove mentioned examples of information recorded in the universal dataare only to provide a better illustration, and the universal data mayalso include other information such as errors (E, E′, E″) associatedwith each regression analysis. More description may be found in, forexample, International Application No. PCT/CN2015/083334 filed Jul. 3,2015 and International Application No. PCT/CN2015/096498 filed Dec. 5,2015, which are hereby incorporated by reference.

As used herein, a subject may refer to a person or animal whose signalor information is acquired and whose physiological parameter isacquired, estimated, or monitored. Merely by way of example, a subjectmay be a patient whose cardiovascular signals are acquired, and bloodpressure estimated or monitored based on the acquired cardiovascularsignals.

One or more algorithms 121 in the server 120 may be applied in dataprocessing or analysis, as described elsewhere in the presentdisclosure. The description of the server 120 above is provided forillustration purposes, and not intended to limit the scope of thepresent disclosure. The server 120 may have a different structure orconfiguration. For example, algorithms 121 are not stored in the server120; instead, the algorithms 121 may be stored locally at the terminal140. Furthermore, a library 1100 may also be stored at the terminal 140.

The external data sources 130 may include a variety of organizations,systems, and devices, or the like, or a combination thereof. Exemplarydata sources 130 may include a medical institution 131, a researchfacility 132, a conventional device 133, and a peripheral device 134, orthe like, or a combination thereof. The medical institution 131 or theresearch facility 132 may provide, for example, personal medicalrecords, clinical test results, experimental research results,theoretical or mathematical research results, algorithms suitable forprocessing data, or the like, or a combination thereof. The conventionaldevice 133 may include a cardiovascular signal measuring device, such asa mercury sphygmomanometer. A peripheral device 134 may monitor and/ordetect one or more types of variables including, for example,temperature, humidity, user or subject input, or the like, or acombination thereof. The above mentioned examples of the external datasources 130 and data types are provided for illustration purposes, andnot intended to limit the scope of the present disclosure. For instance,the external data sources 130 may include other sources and other typesof data, such as genetic information relating to a subject or hisfamily.

The terminal 140 in the system 100 may be configured for processing atleast some of the measured signals, estimating a physiological parameterof interest based on the measured cardiovascular signals, displaying aresult including the physiological parameter of interest in the form of,for example, an image, storing data, controlling access to the system100 or a portion thereof (for example, access to the personal datastored in the system 100 or accessible from the system 100), managinginput-output from or relating to a subject, or the like, or acombination thereof. The terminal 140 may include, for example, a mobiledevice 141 (for example, a smart phone, a tablet, a laptop computer, orthe like), a personal computer 142, other devices 143, or the like, or acombination thereof. Other devices 143 may include a device that maywork independently, or a processing unit or processing module assembledin another device (for example, an intelligent home terminal). Merelybyway of example, the terminal 140 includes a CPU or a processor in ameasuring device 110. In some embodiments, the terminal 140 may includean engine 200 as described in FIG. 2, and the terminal 140 may alsoinclude a measuring device 110.

The network 150 may be a single network or a combination of differentnetworks. For example, the network 150 may be a local area network(LAN), a wide area network (WAN), a public network, a private network, aproprietary network, a Public Telephone Switched Network (PSTN), theInternet, a wireless network, a virtual network, or any combinationthereof. The network 150 may also include various network access points,for example, wired or wireless access points such as base stations orInternet exchange points (not shown in FIG. 1), through which a datasource or any component of the system 100 described above may connect tothe network 150 in order to transmit information via the network 150.

Various components of or accessible from the system 100 may include amemory or electronic storage media. Such components may include, forexample, the measuring device 110, the server 120, the external datasources 130, the terminal 140, peripheral equipment 240 discussed inconnection with FIG. 2, or the like, or a combination thereof. Thememory or electronic storage media of any component of the system 100may include one or both of a system storage (for example, a disk) thatis provided integrally (i.e. substantially non-removable) with thecomponent, and a removable storage that may be removably connected tothe component via, for example, a port (for example, a USB port, afirewire port, etc.) or a drive (for example, a disk drive, etc.). Thememory or electronic storage media of any component of the system 100may include or be connectively operational with one or more virtualstorage resources (for example, cloud storage, a virtual privatenetwork, and/or other virtual storage resources).

The memory or electronic storage media of the system 100 may include adynamic storage device that may store information and instructions to beexecuted by the processor of a system-on-chip (SoC, for example, achipset including a processor), other processors (or computing units),or the like, or a combination thereof. The memory or electronic storagemedia may also be used to store temporary variables or otherintermediate information during execution of instructions by theprocessor(s). Part of or the entire memory or electronic storage mediamay be implemented as Dual In-line Memory Modules (DIMMs), and may beone or more of the following types of memory: static random accessmemory (SRAM), Burst SRAM or Synch Burst SRAM (BSRAM), dynamic randomaccess memory (DRAM), Fast Page Mode DRAM (FPM DRAM), Enhanced DRAM(EDRAM), Extended Data Output RAM (EDO RAM), Extended Data Output DRAM(EDO DRAM), Burst Extended Data Output DRAM (BEDO DRAM), Enhanced DRAM(EDRAM), synchronous DRAM (SDRAM), JEDECSRAM, PCIOO SDRAM, Double DataRate SDRAM (DDR SDRAM), Enhanced SDRAM (ESDRAM), Sync Link DRAM(SLDRAM), Direct Rambus DRAM (DRDRAM), Ferroelectric RAM (FRAM), or anyother type of memory device. The memory or electronic storage media mayalso include read-only memory (ROM) and/or another static storage devicethat may store static information and instructions for the processor ofthe SoC and/or other processors (or computing units). Further, thememory or electronic storage media may include a magnetic disk, opticaldisc or flash memory devices to store information and instructions.

In some embodiments, the SoC may be part of a core processing orcomputing unit of a component of or accessible from the system 100. TheSoC may receive and process input data and instructions, provide outputand/or control other components of the system. In some embodiments, theSoC may include a microprocessor, a memory controller, a memory, and aperipheral component. The microprocessor may further include a cachememory (for example, SRAM), which along with the memory of the SoC maybe part of a memory hierarchy to store instructions and data. Themicroprocessor may also include one or more logic modules such as afield programmable gate array (FPGA) or other logic array. Communicationbetween the microprocessor in the SoC and memory may be facilitated bythe memory controller (or chipset), which may also facilitate incommunicating with the peripheral component, such as a counter-timer, areal-time timer, a power-on reset generator, or the like, or acombination thereof. The SoC may also include other componentsincluding, for example, a timing source (for example, an oscillator, aphase-locked loop, or the like), a voltage regulator, a power managementcircuit, or the like, or a combination thereof.

Merely byway of example, the system 100 may include a wearable orportable device. The wearable or portable device may include a SoC and aplurality of sensors. Exemplary sensors may include a photoelectricsensor, a conductance sensor, or the like, or a combination thereof. TheSoC may process signals acquired through at least some of the pluralityof sensors. The acquired signals may be various physiological signalsincluding, for example, photoplethysmograph (PPG), electrocardiograph(ECG), or the like, or a combination thereof. The SoC may calculate aphysiological parameter of interest based on the acquired signals.Exemplary physiological parameters of interest may be blood pressure,blood oxygen level, ECG information, heart rate, or the like, or acombination thereof.

In some embodiments, the external data source 130 may receive data fromthe measuring device 110, the sever 120, the terminal 140, or the like,or any combination by the network 150. Merely by way of example, theexternal data source 130 (for example, a medical institution, or a smarthome system, or the like) may receive information relating to a subject(for example, location information, data from the cloud sever or aterminal, or the like, or a combination thereof) based on the datareceived from the measuring devices 110 or the terminals 140. In someembodiments, the measuring device 110 may receive data from the sever120, the external data source 130, or the like, or any combination, viathe network 150. Merely by way of example, the measuring device 110 mayreceive the information relating to a subject (for example, acurrent/historical health condition of a subject, medications thesubject is taking, medical treatment the subject is undertaking,current/historical diets, current emotion status, historicalphysiological parameters (for example, PTT, SBP, DBP) relating to thesubject, or the like, or a combination thereof). Furthermore, theterminal 140 may receive data from the measuring device 110, the server120, the external data source 130, or the like, or a combinationthereof.

FIG. 1 is a specific example of the system 100, and the configuration ofthe system 100 is not limited to that illustrated in FIG. 1. Forexample, a server 120 may be omitted, migrating all of its functions toa terminal 140. In another example, a server 120 and a terminal 140 mayboth be omitted, migrating all of their functions to a measuring device110. The system may include various devices or combinations of devicesin different embodiments.

In an example, the system may include a wearable or portable device anda mobile device (for example, a smart phone, a tablet, a laptopcomputer, or the like). The wearable or portable device may be used toacquire physiological signals, environmental information, or the like,or a combination thereof. The mobile device may be used to receive thesignals or information acquired by the wearable or portable device. Themobile device may calculate one or more physiological parameters ofinterest based on the acquired signals or information, as well asrelevant data retrieved from another source (for example, from a server,a memory incorporated in the wearable or portable device, a memoryincorporated in the mobile device, etc.). The retrieved relevant datamay include, for example, current/historical information stored on theserver. Exemplary current/historical information may include acurrent/historical health condition of a subject, current/historicalmedications the subject is/was taking, current/historical medicaltreatment the subject is/was undertaking, current/historical diets,current/historical emotion status, current/historical physiologicalparameters (for example, PTT, SBP, DBP, ECG information, heart rate,blood oxygen level) relating to the subject, or the like, or acombination thereof. The wearable or portable device, or the mobiledevice may display or report, or store at least some of the acquiredsignals, information, the retrieved relevant data, the calculated one ormore physiological parameters of interest, or the like, or a combinationthereof. The display or report may be provided to a subject, a userother than the subject, a third party, the server, or another device.

In another example, the system may include a wearable or portable devicethat may perform functions including: acquiring physiological signals orenvironmental information, retrieving relevant data from another source(for example, from a server, a memory incorporated in the wearable orportable device, etc.), calculating one or more physiological parametersof interest based on the acquired signals, information, or the retrievedrelevant data, and displaying, reporting, or storing at least some ofthe acquired signals, information, the retrieved relevant data, thecalculated one or more physiological parameters of interest, or thelike, or a combination thereof. The display or report may be provided toa subject, a user other than the subject, a third party, the server, oranother device.

In a further example, the system may include a wearable or portabledevice that may perform functions including: acquiring physiologicalsignals and environmental information, communicating with a server totransmit at least some of the acquired signals or information to theserver such that the server may calculate one or more physiologicalparameters of interest, receiving the calculated one or morephysiological parameters of interest from the server, displaying,reporting or storing at least some of the acquired signals, information,the calculated one or more physiological parameters of interest, or thelike, or a combination thereof. The display or report may be provided toa subject, a user other than the subject, a third party, the server, oranother device. In some embodiments, the communication between thewearable or portable device and the server may be achieved by way of thewearable or portable device being connected to a network (for example,the network 150). In some embodiments, the communication between thewearable or portable device and the server may be achieved via acommunication device (for example, a mobile device such as a smartphone, a tablet, a laptop computer, or the like) that communicates withboth the wearable or portable device and the server.

In still a further example, the system may include a wearable orportable device, a mobile device (for example, a smart phone, a tablet,a laptop computer, or the like), and a server. The wearable or portabledevice may be used to acquire physiological signals, environmentalinformation, or the like, or a combination thereof. The mobile devicemay be used to receive the signals or information acquired by thewearable or portable device, and may calculate one or more physiologicalparameters of interest based on the received signals and/or informationretrieved from the wearable or portable device, as well as relevant dataretrieved from, for example, a server, a memory incorporated in thewearable or portable device or incorporated in the mobile device. Themobile device may display, report, or store at least some of theacquired signals, information, the retrieved relevant data, thecalculated one or more physiological parameters of interest, or thelike, or a combination thereof. The display or report may be provided toa subject, a user other than the subject, a third party, the server, oranother device.

In still a further example, the system may include an integratedclinical device or a household device. The integrated device may bewearable or portable. The integrated device may be used to acquirephysiological signals, environmental information, or the like, or acombination thereof. The integrated device may further include an outputdevice that may display, report, or output at least some of the acquiredsignals, information, the retrieved relevant data, the calculated one ormore physiological parameters of interest, or the like, or a combinationthereof. The display or report may be provided to a subject, a userother than the subject, a third party, the server, or another device.The integrated device may perform one or more measurements forcalibrating the integrated device.

Instill a further example, the system may include an integrated clinicaldevice or a household device and a server. The integrated device may bewearable or portable. The integrated device may perform functionsincluding: acquiring physiological signals and environmentalinformation, communicating with a server to transmit at least some ofthe acquired signals or information to the server such that the servermay calculate one or more physiological parameters of interest,receiving the calculated one or more physiological parameters ofinterest from the server, displaying, reporting or storing at least someof the acquired signals, information, the calculated one or morephysiological parameters of interest, or the like, or a combinationthereof. The display or report may be provided to a subject, a userother than the subject, a third party, the server, or another device.The integrated device may perform one or more measurements forcalibrating the integrated device. In some embodiments, thecommunication between the integrated clinical device or the householddevice and the server may be achieved by way of the integrated clinicaldevice or the household device being connected to a network (forexample, the network 150). In some embodiments, the communicationbetween the integrated device and the server may be achieved via acommunication device (for example, a mobile device such as a smartphone, a tablet, a laptop computer, or the like) that communicates withboth the wearable or portable device and the server.

In some embodiments, the system may provide a user interface to allow asubject, a user other than the subject, or an entity to exchangeinformation (including input into or output from the system) with thesystem as disclosed herein. The user interface may be implemented on aterminal device including, for example, a mobile device, a computer, orthe like, or a combination thereof. The user interface may be integratedin the system, e.g., a display device of the system. The access to thesystem may be allowed to one who has an appropriate access privilege. Anaccess privilege may include, for example, a privilege to read some orall information relating to a subject, update some or all informationrelating to a subject, or the like, or a combination thereof. The accessprivilege may be associated with or linked to a set of logincredentials. Merely by way of example, the system may provide threetiers of access privileges. A first tier may include a full accessprivilege regarding information relating to a subject, allowing bothreceiving and updating information relating to a subject. A second tiermay include a partial access privilege regarding information relating toa subject, allowing receiving and updating part of information relatingto a subject. A third tier may include a minimal access privilegeregarding information relating to a subject, allowing receiving orupdating part of information relating to a subject Different logincredentials may be associated with different access privilege to theinformation relating to a subject in the system. As used herein,updating may include providing information that does not exist in thesystem, or modifying pre-existing information with new information.

Merely by way of example, the system may receive information relating toa subject provided via the user interface. The information relating to asubject may include basic information and optional information.Exemplary basic information may include the height, the weight, the age(or the date of birth), the gender, the arm length, the nationality, theoccupation, a habit (for example, a health-related habit such as anexercise habit), the education background, a hobby, the marital status,religious belief, a health-related history (for example, whether asubject has a history of smoking, a food allergy, a drug allergy, amedical treatment history, a family health history, a history of geneticdisease, information regarding a prior surgery, or the like, or acombination thereof), contact information, emergency contact, or thelike, or a combination thereof. Exemplary optional information mayinclude, current health condition of the subject, medications thesubject is taking, a medical treatment the subject is undertaking, diet.The system may receive, via the user interface, information relating toa specific measurement of, for example, a physiological parameter ofinterest. Examples of such information may include the motion state ofthe subject at or around the acquisition time (defined elsewhere in thepresent disclosure), the emotional state at or around the acquisitiontime, the stress level at or around the acquisition time, or the like,or a combination thereof. The system may receive, via the userinterface, one or more options or instructions. In some embodiments, theoptions or instructions may be provided by a subject or a user otherthan the subject answering questions or making selections in response toquestions or prompts by the system. In one example, the options orinstructions may include a measurement frequency (for example, once aweek, once a month, twice a week, twice a month, once a day, twice aday, or the like), a preferred format of the presentation of informationto the subject or a user other than the subject (for example, email, avoice message, a text message, an audio alert, haptic feedback, or thelike, or a combination thereof). In another example, the options orinstructions may include information relating to calculating parametersof interest, for example, rules regarding how to select a model, afunction, calibration data, or the like, or a combination thereof.

In some embodiments, the system may provide, via the user interface,information to a subject, or a user other than the subject. Exemplaryinformation may include an alert, a recommendation, a reminder, or thelike, or a combination thereof. In one example, an alert may be providedor displayed to the subject or a user other than the subject if atriggering event occurs. Exemplary triggering events may be that atleast some of the acquired information or a physiological parameter ofinterest exceeds a threshold. Merely by way of example, a triggeringevent may be that the acquired heart rate exceeds a threshold (forexample, higher than 150 beats per minute, lower than 40 beats perminute, or the like). As another example, a triggering event may be thatthe physiological parameter of interest, for example, an estimated bloodpressure, exceeds a threshold. In another example, a recommendation maybe provided or displayed to the subject or a user other than thesubject. Exemplary recommendations may be a request to input specificdata (for example, basic information, optional information, updatedparameters of interest, updated models, updated functions, updatedoptions and instructions, or the like, or a combination thereof). Areminder may be provided or displayed to the subject or a user otherthan the subject. Exemplary reminders may include a reminder to take aprescription medication, take a rest, take a measurement of aphysiological parameter of interest, or the like, or a combinationthereof.

In some embodiments, the system may communicate with the subject, a userother than the subject, and/or a third party through the user interface.Exemplary third parties may be a doctor, a healthcare worker, a medicalinstitution, a research facility, a peripheral device of the subject ora user well-connected to the subject, or the like. Exemplarycommunications may relate to the health conditions of the subject, adietary habit, an exercise habit, a prescription medication,instructions or steps to conduct a measurement, or the like, or acombination thereof. In some embodiments, a user interface accessible toor by a third party may be the same as, or different from a userinterface accessible to or by a subject. In one example, an output ordata may be transmitted to a third party (for example, a computer, aterminal at a doctor's office, a hospital where a health care provideris located and the health condition of the subject is being monitored,or the like, or a combination thereof). The third party may providefeedback information or instructions related to the output informationvia the user interface. Merely by way of example, a third party mayreceive information regarding one or more physiological parameters ofinterest relating to a subject, and accordingly provide a recommendationof actions to be taken by the subject (for example, to take aprescription medication, to take a rest, to contact or visit the thirdparty, or the like, or a combination thereof); the system may relay therecommendation to the subject.

FIG. 2 shows an exemplary diagram including the engine 200. The engine200 may be configured for acquiring one or more signals and calculatingor estimating one or more physiological parameters of interest based onthe acquired signals. As illustrated, the engine 200 may be connected toor otherwise communicate with, for example, peripheral equipment 240,and the server 120. The engine 200 may include an informationacquisition module 210, an analysis module 220, and an output module230. The information acquisition module 210 may be configured foracquiring a signal or information relating to a subject, for example, aphysiological signal, information relating to the health condition ofthe subject, or the like, or a combination thereof. The analysis module220 may be configured for analyzing the acquired signal or information,or determining or estimating a physiological parameter of interest, orboth. The output module 230 may be configured for outputting theacquired signal or information, the physiological parameter of interest,or the like, or a combination thereof. As used herein, a module may havean independent processor, or use system shared processor(s). Theprocessor(s) may perform functions according to instructions related tovarious modules. For example, the analysis module 220, according torelevant instructions, may retrieve acquired signals and performcalculations to obtain one or more physiological parameter of interest.

The information acquisition module 210 may be configured for acquiring asignal or information from or relating to one or more subjects. As usedherein, acquiring may be achieved by way of receiving a signal orinformation sensed, detected, or measured by, for example, a sensor, orby way of receiving an input from a subject or from a user other thanthe subject (for example, a doctor, a care provider, a family memberrelating to the subject, or the like, or a combination thereof). Forbrevity, an acquired signal or information may be referred to asacquired information. As used herein, information may include a signalrelating to a subject that is acquired by a device including, forexample, a sensor, environmental information that is acquired by adevice including, for example, a sensor, information that is acquiredotherwise including, for example, from an input by a subject or a userother than the subject, a processed or pre-treated information that isacquired as described, or the like, or a combination thereof. Exemplarysensors may include an electrode sensor, an optical sensor, aphotoelectric sensor, a pressure sensor, an accelerometer, a gravitysensor, a temperature sensor, a moisture sensor, or the like, or acombination thereof.

Exemplary acquired information may include physiological information. Inthe exemplary context of determining blood pressure, the physiologicalinformation may include a cardiovascular signal. Exemplarycardiovascular signals may include a photoplethysmogram (PPG) signal, anelectrocardiogram (ECG) signal, a ballistocardiogram (BCG) signal, ablood pressure (BP), a systolic blood pressure (SBP), a diastolic bloodpressure (DBP), a pulse rate (PR), a heart rate (HR), a heart ratevariation (HRV), cardiac murmur, blood oxygen saturation, a density ofblood, a pH value of the blood, a bowel sound, a brainwave, a fatcontent, a blood flow rate, or the like, or a combination thereof.Exemplary acquired information may include information regarding asubject, for example, the height, the weight, the age, the gender, thebody temperature, the arm length, an illness history, or the like, or acombination thereof. Exemplary acquired information may includeinformation from or relating to the ambient surrounding a subject(referred to as environmental information) at or around the acquisitiontime. Exemplary environmental information may include temperature,humidity, air pressure, an air flow rate, an ambient light intensity, orthe like, or a combination thereof. As used herein, the acquisition timemay refer to a time point or a time period when information relating tothe subject, for example, physiological information of the subject, isacquired.

The information acquisition module 210 may receive or load informationfrom the peripheral equipment 240, the server 120, or other devices (notshown) including, for example, an ECG monitor, a PPG monitor, arespiratory monitor, a brainwave monitor, a blood oxygen monitor, ablood glucose monitor, and a device having similar functions. In thedisclosure, the term “monitor” and the term “sensor” may be usedinterchangeably. Examples of peripheral equipment 240 may include asmart watch, an earphone, a pair of glasses, a bracelet, a necklace, orthe like, or a combination thereof. The peripheral equipment 240, theserver 120, or other devices may be local or remote. For example, theserver 120 and the engine 200 may be connected through a local areanetwork (LAN), or Internet. The peripheral equipment 240 and the engine200 may be connected through a local area network, or Internet. Otherdevices and the engine 200 may be connected through a local areanetwork, or Internet. The information transmission between theinformation acquisition module 210 and the peripheral equipment 240, theserver 120, or such other devices may be via a wired connection, awireless connection, or the like, or a combination thereof.

The information acquisition module 210 may receive information providedby a subject or a user other than the subject via, for example, an inputdevice. The input device may include but is not limited to a keyboard, atouch screen (for example, with haptics or tactile feedback), a speechinput device, an eye tracking input device, a brain monitoring system,or the like, or a combination thereof. The information received throughthe input device may be transmitted to a processor, via, for example, abus, for further processing. The processor for further processing theinformation obtained from the input device may be a digital signalprocessor (DSP), a SoC (system on the chip), or a microprocessor, or thelike, or the combination thereof. Other types of input device mayinclude cursor control device, such as a mouse, trackball, or cursordirection keys to convey information about direction and/or commandselections, for example, to the processor.

The description of the information acquisition module 210 is intended tobe illustrative, and not to limit the scope of the present disclosure.Many alternatives, modifications, and variations will be apparent tothose skilled in the art. The features, structures, methods, and othercharacteristics of the exemplary embodiments described herein may becombined in various ways to obtain additional and/or alternativeexemplary embodiments. For example, a storage unit (not shown in FIG. 2)may be added to the information acquisition module 210 for storing theacquired information.

The analysis module 220 may be configured for analyzing acquiredinformation. The analysis module 220 may be connected to or otherwisecommunicate with one or more information acquisition modules 210-1,210-2, . . . , 210-N to receive at least part of the acquiredinformation. The analysis module 220 may be configured for performingone or more operations including, for example, a pre-treatment, acalculation, a calibration, a statistical analysis, or the like, or acombination thereof. Anyone of the operations may be performed based onat least some of the acquired information, or an intermediate resultfrom another operation (for example, an operation performed by theanalysis module 220, or another component of the system 100). Forinstance, the analysis may include one or more operations includingpre-treating at least part of the acquired information, identifying acharacteristic point or feature of the acquired information or thepre-treated information, calculating an intermediate result based on theidentified characteristic point or feature, performing a calibration,analyzing the information regarding the subject provided by the subjector a user other than the subject, analyzing the information regardingthe ambient environment surrounding the subject at or around theacquisition time, estimating a physiological parameter of interest, orthe like, or a combination thereof.

Some operations of the analysis may be performed in parallel or inseries. As used herein, a parallel performance may indicate that someoperations of the analysis may be performed at or around the same time;a serial performance may indicate that some operations of the analysismay commence or be performed after other operations of the analysis havecommenced or finished. In some embodiments, a serial performance of twooperations may indicate that one operation commences after the otheroperation has finished. In some embodiments, a serial performance of twooperations may indicate that one operation commences after the otheroperation has commenced, and the two operations partially overlap. Insome embodiments, at least two operations of an analysis may beperformed in parallel. In some embodiments, at least two operations ofan analysis may be performed in series. In some embodiments, some of theoperations of an analysis may be performed in parallel, and some of theoperations may be performed in series.

The analysis, or some operations of the analysis, may be performed realtime, i.e. at or around the acquisition time. The analysis, or someoperations of the analysis, may be performed after a delay since theinformation is acquired. In some embodiments, the acquired informationis stored for analysis after a delay. In some embodiments, the acquiredinformation is pre-treated and stored for further analysis after adelay. The delay may be in the order of seconds, or minutes, or hours,or days, or longer. After the delay, the analysis may be triggered by aninstruction from a subject or a user other than the subject (forexample, a doctor, a care provider, a family member relating to thesubject, or the like, or a combination thereof), an instruction storedin the system 100, or the like, or a combination thereof. Merely by wayof example, the instruction stored in the system 100 may specify theduration of the delay, the time the analysis is to be performed, thefrequency the analysis is to be performed, a triggering event thattriggers the performance of the analysis, or the like, or a combinationthereof. The instruction stored in the system 100 may be provided by asubject or a user other than the subject. An exemplary triggering eventmay be that at least some of the acquired information or a physiologicalparameter of interest exceeds a threshold. Merely by way of example, atriggering event may be that the acquired heart rate exceeds a threshold(for example, higher than 150 beats per minute, lower than 40 beats perminute, or the like). As used herein, “exceed” may be larger than orlower than a threshold. As another example, a triggering event may bethat the physiological parameter of interest, for example, an estimatedblood pressure, exceeds a threshold.

The analysis module 220 may be centralized or distributed. A centralizedanalysis module 220 may include a processor (not shown in FIG. 2). Theprocessor may be configured for performing the operations. A distributedanalysis module 220 may include a plurality of operation units (notshown in FIG. 2). The operation units may be configured for collectivelyperforming the operations of a same analysis. In the distributedconfiguration, the performance of the plurality of operation units maybe controlled or coordinated by, for example, the server 120.

The acquired information, an intermediate result of the analysis, or aresult of the analysis (for example, a physiological parameter ofinterest) may be analog or digital. In an exemplary context of bloodpressure monitoring, the acquired information, an intermediate result ofthe analysis, or a result of the analysis (for example, a physiologicalparameter of interest) may include, for example, a PPG signal, an ECGsignal, a BCG signal, a BP, a SBP, a DBP, a PR, a HR, a HRV (heart ratevariation), cardiac murmur, blood oxygen saturation (or referred to asblood oxygen level), a blood density, a pH value of the blood, a bowelsound, a brainwave, a fat content, a blood flow rate, or the like, or acombination thereof.

A result of the analysis, for example, a physiological parameter ofinterest regarding a subject, may be influenced by various factors orconditions including, for example, an environmental factor, a factor dueto a physiological condition of a subject, a factor due to apsychological condition of a subject, or the like, or a combinationthereof. One or more of such factors may influence the accuracy of theacquired information, the accuracy of an intermediate result of theanalysis, the accuracy of a result of the analysis, or the like, or acombination thereof. For instance, a physiological parameter of interestmay be estimated based on a correlation with the acquired information; afactor due to a physiological condition may cause a deviation from thecorrelation; the factor may influence the accuracy of the physiologicalparameter of interest that is estimated based on the correlation. Merelyby way of example, a cardiovascular signal relating to a subject mayvary with, for example, time, the psychological condition of thesubject, or the like, or a combination thereof. The correlation betweena cardiovascular signal with a physiological parameter (for example, thecorrelation between a PPT value and a blood pressure) of a subject mayvary with, for example, the physiological condition of the subject, thepsychological condition of the subject, the ambient surrounding thesubject, or the like, or a combination thereof. Such an influence may becounterbalanced or compensated in the analysis.

In an analysis, information relating to an influencing condition (forexample, environmental information, a physiological condition, apsychological condition, or the like) may be acquired, and a correctionor adjustment may be made accordingly in the analysis process. Merely byway of example, the correction or adjustment may be by way of acorrection factor. For instance, an environmental correction factor maybe introduced into the analysis based on acquired environmentalinformation from or relating to the ambient surrounding a subject at oraround the acquisition time. Exemplary environmental information mayinclude one or more of temperature, humidity, air pressure, an air flowrate, an ambient light intensity, or the like. Exemplary environmentalcorrection factors may include one or more of a temperature correctionfactor, a humidity correction factor, an air pressure correction factor,an air flow rate correction factor, an ambient light intensitycorrection factor, or the like. As another example, the correction oradjustment may be by way of performing a calibration of the correlation(for example, a calibrated model, a calibrated function, or the like)used to estimate the physiological parameter of interest. As a furtherexample, the correction or adjustment may be by way of choosing, basedon information relating to an influencing condition, a correlation froma plurality of correlations used to estimate the physiological parameterof interest.

This description of the analysis module 220 is intended to beillustrative, and not to limit the scope of the present disclosure. Manyalternatives, modifications, and variations will be apparent to thoseskilled in the art. The features, structures, methods, and othercharacteristics of the exemplary embodiments described herein may becombined in various ways to obtain additional and/or alternativeexemplary embodiments. For example, a cache unit (not shown in FIG. 2)may be added to the analysis module 220 used for storing an intermediateresult or real time signal or information during the processes abovementioned.

The output module 230 may be configured for providing an output. Theoutput may include a physiological parameter of interest, at least someof the acquired information (for example, the acquired information thatis used in estimating the physiological parameter of interest), or thelike, or a combination thereof. The transmission of the output may bevia a wired connection, a wireless connection, or the like, or acombination thereof. The output may be transmitted real-time once theoutput is available for transmission. The output may be transmittedafter a delay since the output is available for transmission. The delaymay be in the order of seconds, or minutes, or hours, or days, orlonger. After the delay, the output may be triggered by an instructionfrom a subject, a user other than the subject, or a related third party,an instruction stored in the system 100, or the like, or a combinationthereof. Merely by way of example, the instruction stored in the system100 may specify the duration of the delay, the time the output is to betransmitted, the frequency output is to be transmitted, a triggeringevent, or the like, or a combination thereof. The instruction stored inthe system 100 may be provided by a subject or a user other than thesubject. An exemplary triggering event may be that the physiologicalparameter of interest or that at least some of the acquired informationexceeds a threshold. Merely by way of example, a triggering event may bethat the acquired heart rate exceeds a threshold (for example, higherthan 150 beats per minute, lower than 40 beats per minute, or the like).As another example, a triggering event may be that the physiologicalparameter of interest, for example, an estimated blood pressure, exceedsa threshold.

The output for transmission may be of, for example, an analog form, adigital form, or the like, or a combination thereof. The output may bein the format of, for example, a graph, a code, a voice message, text,video, an audio alert, a haptic effect, or the like, or a combinationthereof. The output may be displayed on a local terminal, or transmittedto a remote terminal, or both. A terminal may include, for example, apersonal computer (PC), a desktop computer, a laptop computer, a smartphone, a smart watch, or the like, or a combination thereof. Merely byway of example, an output may be displayed on a wearable or portabledevice a subject wears, and also transmitted to a computer or terminalat a doctor's office or a hospital where a health care provider islocated and monitors the health condition of the subject.

The output module 230 may include or communicate with a display devicethat may display output or other information to a subject or a userother than the subject. The display device may include a liquid crystaldisplay (LCD), a light emitting diode (LED)-based display, or any otherflat panel display, or may use a cathode ray tube (CRT), a touch screen,or the like. A touch screen may include, for example, a resistance touchscreen, a capacity touch screen, a plasma touch screen, a vectorpressure sensing touch screen, an infrared touch screen, or the like, ora combination thereof.

The peripheral equipment 240 may include any kind of local or remoteapparatuses or devices relating to or communicating with the system 100,or a portion thereof. For example, the peripheral equipment 240 mayinclude a storage device, display equipment, a measuring device, aninput device, or the like, or a combination thereof.

In some embodiments, a storage module (not shown in FIG. 2) or a storageunit (not shown in FIG. 2) may be integrated in the engine 200. In someembodiments, a storage unit (not shown in FIG. 2) may be integrated inany one of the information acquisition module 210, the analysis module220, or the output module 230. The storage module (not shown in FIG. 2)or the storage unit (not shown in FIG. 2) may be used for storing anintermediate result, or a result of an analysis. The storage module (notshown in FIG. 2) or the storage unit (not shown in FIG. 2) may be usedas a data cache. The storage module (not shown in FIG. 2) or the storageunit (not shown in FIG. 2) may include a hard disk, a floppy disk,selectron storage, RAM, DRAM, SRAM bubble memory, thin film memory,magnetic plated wire memory, phase change memory, flash memory, clouddisk, or the like, or a combination thereof. The storage module (notshown in FIG. 2) or the storage unit (not shown in FIG. 2) may includememory or electronic storage media described in connection with FIG. 1and elsewhere in the present disclosure.

In some embodiments, the engine 200 does not include a storage module ora storage unit, and the peripheral equipment 240 or the server 120 maybe used as a storage device accessible by the engine 200. The server 120may be a cloud server providing cloud storage. As used herein, cloudstorage is a model of data storage where digital data are stored inlogical pools, physical storage spanning multiple servers (and oftenlocated at multiple locations). The physical environment including, forexample, the logical pools, the physical storage spanning multipleservers may be owned and managed by a hosting company. The hostingcompany may be responsible for keeping the data available andaccessible, and the physical environment protected and running. Suchcloud storage may be accessed through a cloud service, a web serviceapplication programming interface (API), or by applications that utilizethe API. Exemplary applications include cloud desktop storage, a cloudstorage gateway, a Web-based content management system, or the like, ora combination thereof. The server 120 may include a public cloud, apersonal cloud, or both. For example, the acquired information may bestored in a personal cloud that may be accessed after authorization byway of authenticating, for example, a username, a password, a secretcode, or the like, or a combination thereof. Non personalizedinformation including, for example, methods or calculation models, maybe stored in a public cloud. No authorization or authentication isneeded to access the public cloud. The information acquisition module210, the analysis module 220 and the output module 230 may retrieve orload information or data from the public cloud or the personal clouds.Any one of these modules may transmit signals and data to the publiccloud or personal cloud.

Connection or transmission between any two of the informationacquisition module 210, the analysis module 220, and the output module230 may be via a wired connection, a wireless connection, or the like,or a combination thereof. At least two of these modules may be connectedwith different peripheral equipment. At least two of these modules maybe connected with the same peripheral equipment. The peripheralequipment 240 may be connected with one or more modules via a wiredconnection, a wireless connection, or the like, or a combinationthereof. Those skilled in the art should understand that the aboveembodiments are only utilized to describe the invention in the presentdisclosure. There are many modifications and variations to the presentdisclosure without departing the spirit of the invention disclosed inthe present disclosure. For example, the information acquisition module210 and the output module 230 may be integrated in an independent moduleconfigured for acquiring and outputting signals or results. Theindependent module may be connected with the analysis module 220 via awired connection, a wireless connection, or the like, or a combinationthereof. The three modules in the engine 200 may be partially integratedin one or more independent modules or share one or more units.

The connection or transmission between the modules in the system 100, orbetween the modules and the peripheral equipment 240, or between thesystem and the server 120 should not be limited to the descriptionsabove. All the connections or transmissions may be used in combinationor may be used independently. The modules may be integrated in anindependent module, i.e. functions of the modules may be implemented bythe independent module. Similarly, one or more modules may be integratedon a single piece of peripheral equipment 240. Any one of theconnections or transmissions mentioned above may be via a wiredconnection, a wireless connection, or the like, or a combinationthereof. For example, the wired connection or wireless connection mayinclude, for example, a wire, a cable, satellite, microwave, blue tooth,radio, infrared, or the like, or a combination thereof.

The engine 200 may be implemented on one or more processors. The modulesor units of the engine 200 may be integrated in one or more processors.For example, the information acquisition module 210, the analysis module220, and the output module 230 may be implemented on one or moreprocessors. The one or more processors may transmit signals or data witha storage device (not shown in FIG. 2), the peripheral equipment 240,and the server 120. The one or more processors may retrieve or loadsignals, information, or instructions from the storage device (not shownin FIG. 2), the peripheral equipment 240, or the server 120, and processthe signals, information, data, or instructions, or a combinationthereof, to calculate one or more physiological parameters of interest.The one or more processors may also be connected or communicate withother devices relating to the system 100, and transmit or share signals,information, instructions, the physiological parameters of interest, orthe like with such other devices via, for example, a mobile phone APP, alocal or remote terminal, or the like, or a combination thereof.

FIG. 3 is a flowchart showing an exemplary process for estimating aphysiological parameter of interest according to some embodiments of thepresent disclosure. Information regarding a subject may be acquired instep 310. The information acquisition may be performed by theinformation acquisition module 210. The acquired information may includephysiological information of the subject, environmental informationrelating to the ambient surrounding the subject at or around theacquisition time, information provided by the subject or a user otherthan the subject. The acquired information may include a PPG signal, anECG signal, a pulse rate, a heart rate, a heart rate variation, bloodoxygen saturation, respiration, muscle state, skeleton state, abrainwave, a blood lipid level, a blood sugar level, the height, theweight, the age, gender, the body temperature, the arm length, anillness history, the room temperature, humidity, air pressure, an airflow rate, the ambient light intensity, or the like, or a combinationthereof. At least some of the acquired information may be analyzed at320. Via the analysis, various features of at least some of the acquiredinformation may be identified. For example, the acquired information mayinclude a PPG signal and an ECG signal; the identified features of thesesignals may include, for example, waveform, characteristic points, peakpoints, valley points, amplitude, time intervals, phase, frequencies,cycles, or the like, or a combination thereof. Analysis based on theidentified features may be carried out in step 320. For example, thephysiological parameter of interest may be calculated or estimated basedon the identified features. The physiological parameter of interestestimated based on the acquired PPG signal and ECG signal may include,for example, the BP, the SBP, the DBP, the blood oxygen level, or thelike, or a combination thereof. The physiological parameter of interestmay be outputted in step 330. Some of the acquired information may beoutputted in step 330. The output may be displayed to the subject or auser other than the subject, printed, stored in a storage device or theserver 120, transmitted to a device further process, or the like, or acombination thereof. It should be noted that after analysis in step 320,a new acquisition step may be performed in step 310.

This description is intended to be illustrative, and not to limit thescope of the present disclosure. Many alternatives, modifications, andvariations will be apparent to those skilled in the art. The features,structures, methods, and other characteristics of the exemplaryembodiments described herein may be combined in various ways to obtainadditional and/or alternative exemplary embodiments. For example, apre-treatment step may be added between step 310 and step 320. In thepre-treatment step, the acquired signals may be pre-treated, in order toreduce or remove noise or interferences in the signals originallyacquired. For example, a sophisticated, real-time digital filtering maybe used to reduce or remove high-frequency noise from the PPG or ECGsignal, allowing their features to be accurately identified. Exemplarypre-treatment methods may include low-pass filtering, band-passfiltering, wavelet transform, median filtering, morphological filtering,curve fitting, Hilbert-Huang transform, or the like, or a combinationthereof. Descriptions regarding methods and systems for reducing orremoving noise from a physiological signal, for example, a PPG signal oran ECG signal, may be found in, for example, International PatentApplication Nos. PCT/CN2015/077026 filed Apr. 20, 2015,PCT/CN2015/077025 filed Apr. 20, 2015, and PCT/CN2015/079956 filed May27, 2015, each of which is incorporated by reference. One or more otheroptional steps may be added between step 310 and step 320, or elsewherein the exemplary process illustrated in FIG. 3. Examples of such stepsmay include storing or caching the acquired information.

FIG. 4 is a block diagram illustrating an architecture of an informationacquisition module according to some embodiments of the presentdisclosure. The information acquisition module 210 may be connected toor otherwise communicate with, for example, the peripheral equipment240, the analysis module 220, the output module 230, and the server 120through the network 150. The information acquisition module 210 may beconfigured for acquiring information relating to the subject,information provided by the subject, a user other than the subject,and/or a related third party (for example, a doctor, a healthcareworker, a medical institution, a research facility, a peripheral deviceof the subject or a user well-connected to the subject, or the like),environmental information from the ambient surrounding the subject at oraround the acquisition time, or the like, or a combination thereof. Theinformation acquisition module 210 may include a first acquisition unit410 and a second acquisition unit 420. The first acquisition unit 410may be configured for acquiring a first signal or first informationincluding a first signal relating to the subject. The second acquisitionunit 420 may be configured for acquiring a second signal or secondinformation including a second signal relating to the subject. The firstacquisition unit 410 and the second acquisition unit 420 may acquiresignals in real time. The first signal and the second signal may beacquired simultaneously, at or around the same time. In someembodiments, other than the first acquisition unit 410 and the secondacquisition unit 420, the information acquisition module 210 may includeone or more other acquisition units (not shown in FIG. 4). In someembodiments, the first acquisition unit 410 and the second acquisitionunit 420 may be integrated in an independent module or unit.

In some embodiments, the first acquisition unit 410 may be configuredfor acquiring an ECG signal of the subject. The first acquisition unit410 may include an ECG monitor (not shown in FIG. 4). The ECG monitor(not shown in FIG. 4) may be of any type, e.g., a clinical device, ahouse device, a wearable device, a portable device, or the like. The ECGmonitor (not shown in FIG. 4) may include a plurality of electrodes usedfor recording the variations in the electrical potential relating to thecardiovascular activity of the subject. The electrodes may be arrangedin a 12-lead form, a 5-lead form, a 3-lead form, or the like. Theelectrodes may be located on one or more limbs and/or on the chest ofthe subject. For instance, in the 5-lead form, the electrodes may belocated on the chest of the subject. In some embodiments, the firstacquisition unit 410 may include a control unit (not shown in FIG. 4).The control unit (not shown in FIG. 4) may be configured for controllinga parameter of the acquisition process. The parameter may includesampling frequency, sampling time interval, or the like, or acombination thereof. In some embodiments, the first acquisition unit 410may include a storage unit (not shown in FIG. 4). The storage unit (notshown in FIG. 4) may be used for storing the acquired first signals, theparameters, or the like, or a combination thereof. In some embodiments,the acquired signals, the parameters may be stored in any storage devicedisclosed anywhere in the present disclosure.

In some embodiments, the second acquisition unit 420 may be configuredfor acquiring a PPG signal or acquiring information including a PPGsignal. In some embodiments, the second acquisition unit 420 may includea blood oxygen monitor (not shown in FIG. 4). The blood oxygen monitor(not shown in FIG. 4) may be configured for acquiring the subject'sblood oxygen information using a photoelectric sensor. Blood oxygeninformation may be estimated based on two or more PPG signals. In someembodiments, at least one of the acquired PPG signals together with anECG signal may be used for calculating PTT, which may be used toestimate a blood pressure value based on a model (see, for example,International Patent Application Nos PCT/CN2015/083334 filed Jul. 3,2015 and PCT/CN2015/096498 filed Dec. 5, 2015).

In some embodiments, the blood oxygen monitor (not shown in FIG. 4) mayinclude a single photoelectric sensor, or a sensor array including aplurality of photoelectric sensors. A photoelectric sensor may includeone or more emitting ends and one or more receiving ends. The emittingend may include one or more 25 light sources. A light source may emitone or more of ultrasound, radio, microwave, millimeter wave, infrared,visible, ultraviolet, gamma ray, or X-ray electromagnetic radiation. Asused herein, light may also include any wavelength within the radio,microwave, infrared (IR), visible, ultraviolet (UV), or X-ray spectra,and that any suitable wavelength of electromagnetic radiation may beappropriate for use with the system, device, or apparatus disclosedherein. Merely by way of example, an emitting end may include two lightsources, a red light emitting light source such as a red light emittingdiode (LED), and an IR light emitting light source such as IR LED; theemitting end may emit light into the tissue of a subject at thewavelengths used to calculate a physiological parameter of interest ofthe subject (e.g., blood oxygen information). As used herein, forbrevity, a specific wavelength may also include wavelengths within arange of the specific wavelength. For instance, the red wavelength maybe between approximately 600 nm and approximately 700 nm, and the IRwavelength may be between approximately 800 nm and approximately 1000nm. In embodiments where a sensor array is used, each sensor may emit asingle wavelength. The receiving end may be used for receiving signalsresulting from the emitted lights through the subject. In someembodiments, the second acquisition unit 420 may be configured foracquiring the subject's PPG signals from multiple body locations (forexample, the head, the neck, the chest, the abdomen, the upper arm, thewrist, the waist, the upper leg, the knee, the ankle, or the like, or acombination thereof). In some embodiments, one or more photoelectricsensors may be placed on any one of the multiple body locations. In someembodiments, one or more photoelectric sensor arrays may be placed onany of the multiple body locations.

In some embodiments, the second acquisition unit 420 may include acontrol unit (not shown in FIG. 4) and/or a storage unit (not shown inFIG. 4). Similarly the control unit (not shown in FIG. 4) may beconfigured for controlling the acquisition process of the second signalor second information. The storage unit (not shown in FIG. 4) may beconfigured for storing the acquired signals and/or information.

The information acquisition module 210 may include one or more otheracquisition units (not shown in FIG. 4). For example, an acquisitionunit may be configured for acquiring basic information relating to thesubject, for example, the height, the weight, the age (or the date ofbirth), the gender, the arm length, the nationality, the occupation, ahabit (for example, a health-related habit such as an exercise habit),the education background, a hobby, the marital status, religious belief,a health-related history (for example, whether a subject has a historyof smoking, a food allergy, a drug allergy, a medical treatment history,a family health history, a history of genetic disease, informationregarding a prior surgery, or the like, or a combination thereof),contact information, emergency contact, or the like, or a combinationthereof. The basic information relating to the subject may be providedby the subject, a user other than the subject, or a third party (forexample, a doctor, a healthcare worker, a medical institution, aresearch facility, a peripheral device of the subject or a userwell-connected to the subject, or the like).

In another example, an acquisition unit may be configured for acquiringenvironmental information surrounding the subject, includingtemperature, humidity, air pressure, an air flow rate, an ambient lightintensity, or the like, or a combination thereof. The environmentalinformation may be acquired in a real time mode (for example, at oraround the acquisition time), or may be acquired at a certain timeinterval (for example, independent of the acquisition time).

In a further example, one or more acquisition units may be configuredfor acquiring the subject's EMG signals by way of a pressure sensingmethod, body temperature data by way of a temperature sensing method, orthe like, or a combination thereof. In a further example, an acquisitionunit may be configured for acquiring BCG signals, blood densityinformation, pH value information of the blood, or the like, or acombination thereof.

The one or more acquisition units may communicate with one or moresensors to acquire information sensed, detected or measured by the oneor more sensors. Exemplary sensors include an electrode sensor, anoptical sensor, a photoelectric sensor, a conductance sensor, a pressuresensor, an accelerometer, a gravity sensor, a temperature sensor, amoisture sensor, or the like, or a combination thereof.

Merely by way of example, an optical sensor may include an integratedphotodetector and a light source. The optical sensor may also include anamplifier. The light source may emit radiation of wavelengths of, forexample, the visible spectrum, the infrared region, or the like, or acombination thereof. The photodetector may detect the radiationresulting from light (of a wavelength, or within a range of thewavelength) that impinges upon or into and/or is reflected by a tissue,and reaches the photodetector (or referred to as the reflectedradiation). The optical sensor may be placed at a body location of asubject to detect a pulse-related signal of a subject. For instance, theoptical sensor may be a PPG sensor. In some embodiments, an opticalsensor may include a plurality of light sources, in which a light sourcemay emit light of a wavelength, or within a range of the wavelength.Thus, the plurality of light sources may emit light of variouswavelengths, or within a respective range thereof. For instance, thelight sources may emit a red light and an infrared light. In someembodiments, an optical sensor may include a plurality ofphotodetectors, in which a photodetector may be used to detect thereflected radiation resulting from the light of a wavelength, or withina range of the wavelength. In some embodiments, a photodetector may beused to detect the reflected radiation resulting from the emitted lightof various wavelengths, or within a respective range thereof. Forinstance, a photodetector may be used to detect the reflected radiationresulting from the red light and the infrared light.

In some embodiments, a plurality of PPG sensors may be assembled intoone device. One PPG sensor of the plurality of PPG sensors may include alight source, and a photodetector; the light source may emit light of awavelength, or within a range thereof; the photodetector may be used todetect the reflected radiation resulting from the emitted light (of awavelength, or within a range of the wavelength). The plurality of PPGsensors may include a PPG sensor that includes a red light emittinglight source and a photodetector that may detect the reflected radiationresulting from the red light, and a PPG sensor that includes an infraredlight emitting light source and a photodetector that may detect thereflected radiation resulting from the infrared light. In someembodiments, at least two of the plurality of PPG sensors may be placedat different locations on the body of a subject. For instance, one PPGsensor may be placed on an upper arm of the subject, and another PPGsensor may be placed on a finger of the subject. In some embodiments, atleast two of the plurality of PPG sensors may be placed at or around thesame location on the body of a subject. For instance, two PPG sensorsmay be placed at an upper arm of the subject. In another example, twoPPG sensors may be placed at a finger of the subject. In someembodiments, a device may include a PPG sensor; the PPG sensor mayinclude a plurality of light sources and a photodetector; the lightsources may emit light of various wavelengths, or within a respectiverange thereof; the photodetector may be used to detect the reflectedradiation resulting from the emitted light of various wavelengths, orwithin a respective range thereof.

The device may be a wearable or portable device including, for example,a T-shirt, a smart watch, a wristband, or the like, or a combinationthereof. The device may further include one or more processors orprocessing units. The processor or the processing unit may be configuredfor controlling the process of information acquisition, or may beconfigured for performing one or more operations of any of the modules.Signals or data may be transmitted between sensors placed at differentlocations. The transmission may be via a wireless connection (forexample, WiFi, blue tooth, near-field communication (NFC), or the like,or a combination thereof), a wired connection, or the like, or acombination thereof. For example, signals received by the sensors may betransmitted through a wireless body sensor network (BSN) or anintra-body communication (IBC).

This description is intended to be illustrative, and not to limit thescope of the present disclosure. Many alternatives, modifications, andvariations will be apparent to those skilled in the art. The features,structures, methods, and other characteristics of the exemplaryembodiments described herein may be combined in various ways to obtainadditional and/or alternative exemplary embodiments. For example, theacquisition units may be integrated into an independent unit configuredfor acquiring more than one information or signal relating to thesubject. At least some of the acquisition units may be integrated intoone or more independent units. The one or more acquisition units mayshare a common control unit (not shown in FIG. 4) and/or a commonstorage unit (not shown in FIG. 4).

FIG. 5 is a block diagram illustrating an architecture of a secondacquisition unit 420 according to some embodiments of the presentdisclosure. The second acquisition unit 420 may be configured foracquiring a second signal or second information including a secondsignal relating to the subject. In some embodiments, the secondacquisition unit 420 may acquire a physiological parameter of interest,e.g., the blood oxygen saturation relating to a subject. The secondacquisition unit 420 may include, without limitations to, a firstdetection unit 510, a second detection unit 520, an A/D converter 530, aprocessing unit 540, and a storage unit 550.

The first detection unit 510 and/or the second detection unit 520 may beconfigured for acquiring one or more pulse wave related signals. Thefirst detection unit 510 and the second detection unit 520 may includeat least one emitting end (not shown in FIG. 5) configured for emittingone or more lights toward the subject, and at least one receiving end(not shown in FIG. 5) configured for receiving signals resulting fromthe emitted lights. In some embodiments, the emitting end may include aplurality of light sources, the light source may emit radiation ofwavelengths of, for example, visible spectrum, infrared spectrum,far-infrared spectrum, or the like, or a combination thereof. Merely byway of example, the light source may include a light of a suitablewavelength including, for example, red, green, blue, infrared, purple,yellow, orange, or the like, or a combination thereof. For brevity, thelight source in the first detection unit 510 may be referred to as thefirst light source used for emitting a first light, and the light sourcein the second detection unit 520 may be referred to as the second lightsource used for emitting a second light. The first light source and thesecond light source may be the same or may be different. The first lightsource and the second light source may be constructed separately orcombined to be one integrated light source, or share one optical source.The first light and the second light may be emitted alternately,separately, or simultaneously. The first light and the second light maybe the same or may be different. In some embodiments, the first lightmay be a red light, and the second light may include radiation beams ofa wavelength at the isosbestic point of HbO (oxygenate hemoglobin) andHb (hemoglobin) (e.g., an infrared light).

In some embodiments, the first detection unit 510 and/or the seconddetection unit 520 may include one or more receiving ends (not shown inFIG. 5). Merely by way of example, the first detection unit 510 mayinclude a first receiving end and the second detection unit 520 mayinclude a second receiving end. The first receiving end and the secondreceiving end may be the same or may be different. In some embodiments,the first receiving end and the second receiving end may be integratedin an independent device (e.g., the first detection unit 510, the seconddetection unit 520, or any independent device in the system). In someembodiments, the first detection unit 510 and the second detection unit520 may share one common receiving end. A receiving end may include aphotodetector that may detect the reflected or transmitted radiationresulting from the light impinged upon or into and reflected by ortransmitted through a tissue of the subject that reaches the receivingend.

The A/D converter 530 may be configured for converting an analog signalto a digital signal. The detected signals by the first detection unit510 and/or the second detection unit 520 may be transmitted to the A/Dconverter 530 in real time or after a time delay. Merely by way ofexample, in the case of blood oxygen information acquisition process, atleast two pulse wave related signals (e.g., PPG signals) may bedetected. The PPG signals may be converted to digital signals and theconverted digital signals may be stored in the storage unit 550 or anystorage device disclosed anywhere in the present disclosure. Theconverted digital signals may be used for subsequent process (e.g., acalculation process). In some embodiments, the detected signals may beprocessed by the processing unit 540 and the processed signals may bestored in the storage unit 550. Then the processed signals may be loadedby the A/D converter.

The processing unit 540 may be configured for processing the detectedsignals by the first detection unit 510 and/or the second detection unit520 or the signals stored in the storage unit 550. In some embodiments,the processing may be performed for reducing or removing noise, baselinedrift, artefacts, or other interferences in the detected signals.Exemplary processing techniques may include low-pass filtering,high-pass filtering, band-pass filtering, phase filtering, wavelettransform, median filtering, morphological filtering, curve fitting,Hilbert transform, statistical analysis such as correlation, or thelike, or a combination thereof. More detailed descriptions regardingmethods and systems for reducing or removing noises may be found inInternational Patent Application Nos. PCT/CN2015/077026 filed Apr. 20,2015, PCT/CN2015/077025 filed Apr. 20, 2015, and PCT/CN2015/079956 filedMay 27, 2015, each of which is incorporated by reference.

In some embodiments, the processing may be performed for featureextraction, e.g., a direct current (DC) component of the signal, analternating current (AC) component of the signal, root mean square valuewithin a certain period of time, peak points, valley points, amplitude,or a ratio, logarithm and combination thereof. As used herein, the DCcomponent of the signal may be referred to as a non-pulsatile componentor non-varying component of the signal. As used herein, the AC componentof the signal may be referred to as a pulsatile component or atime-varying component of the signal. In some embodiments, the signalmay be a PPG signal. The PPG signal may be considered as a signalsuperposition of the DC component and the AC component, wherein the ACcomponent may be the pulsatile component or time-varying component ofthe PPG signals, which is due to the expansion and relaxation of theblood; the DC component may be the non-pulsatile component ornon-varying component of the PPG signals, which is due to the absorptionof light by tissue, non-pulsatile blood, and/or venous blood. In someembodiments, the processing may be performed for calculating aphysiological parameter of interest (e.g., blood oxygen information)based on the detected signals and/or the extracted features of thesignals. During the calculation of the physiological parameter ofinterest, the features extracted from the detected pulse wave relatedsignals may be used; a calibration curve equation, mathematicalcalculation model or function may be retrieved. During the calculation,a parameter may be determined, e.g., oxygen saturation coefficient,light attenuation variation, ratio of light attenuation variationbetween wavelengths, or HbO and Hb concentration, or the like. Merely byway of example, when infrared light and red light are applied for thefirst detection unit 510 and the second detection unit 520, a bloodoxygen level (or blood oxygen saturation) may be calculated based on,for example, the resultant PPG signals. The calculation may includeusing a linear or nonlinear model. In some embodiments, the blood oxygenlevel may be estimated or calculated based on the ratio of the ACcomponent of the PPG signals to the DC component of the PPG signals.

The storage unit 550 may be configured for storing data, signal, featurevalue, information or calculation model recorded in the secondacquisition unit 420, generated by the second acquisition unit 420, orinputted into the second acquisition unit 420. The storage unit mayinclude a memory (not shown in FIG. 5) or share the same memory withother unit inside or outside the second acquisition unit 420. Thestorage unit 550 may include one or both of a system storage (forexample, a disk) that is provided integrally (i.e. substantiallynon-removable) with the component, and a removable storage that isremovably connectable to the component via, for example, a port (forexample, a USB port, a firewire port, etc.) or a drive (for example, adisk drive, etc.). The storage unit 550 may include or be connectivelyoperational with one or more virtual storage resources (for example,cloud storage, a virtual private network, and/or other virtual storageresources). The storage unit 550 may include a hard disk, a floppy disk,selectron storage, RAM, DRAM, SRAM bubble memory, thin film memory,magnetic plated wire memory, phase change memory, flash memory, clouddisk, or the like, or a combination thereof. The storage unit 550 may beconnected or otherwise communicate with other modules or units in thesystem, the server 120, and the peripheral equipment 240.

The second acquisition unit 420 may include one or more other units 560.In some embodiments, the other units 560 may include a controller ormicroprocessor or share the same controller or microprocessor with othermodules or units inside or outside the second acquisition unit 420. Thecontroller or microprocessor may be configured for controlling a factoror a condition of operations of the first detection unit 510, the seconddetection unit 520, the A/D converter 530, the processing unit 540 andthe storage unit 550. The factor or the condition may include ON/OFF ofthe detection process, sampling frequency, a detection cycle, aprocessing parameter, or the like, or a combination thereof.

This description is intended to be illustrative, and not to limit thescope of the present disclosure. Many alternatives, modifications, andvariations will be apparent to those skilled in the art. The features,structures, methods, and other characteristics of the exemplaryembodiments described herein may be combined in various ways to obtainadditional and/or alternative exemplary embodiments. For example, thestorage unit 550 may be integrated into any unit described in FIG. 5, orthe storage unit 550 is not necessary while any storage device disclosedanywhere in the present disclosure may be used. In some embodiments, thefirst detection unit 510 and the second detection unit 520 may beintegrated into an independent unit configured for detecting more thanone pulse wave related signal. In some embodiments, at least some of theunits described may be integrated into an independent unit. In someembodiments, the second acquisition unit 420 may further include adisplay device configured for displaying the acquired signals and/ordetermined physiological parameter of interest (e.g., blood oxygensaturation).

FIG. 6 is a block diagram illustrating an architecture of an analysismodule according to some embodiments of the present disclosure. Theanalysis module 220 may be connected to or otherwise communicate with,e.g., the peripheral equipment 240, and the server 120 through thenetwork 150. The analysis module 220 may estimate or calculate aphysiological parameter of interest relating to a subject based onacquired information. The analysis module 220 may include apre-treatment unit 610, a recognition unit 620, a calculation unit 630,and a calibration unit 640.

The pre-treatment unit 610 may be configured for pre-treating theacquired information. The pre-treatment may be performed to reduce orremove noise or interferences in the original signals. For example, asophisticated, real-time digital filtering may reduce or removehigh-frequency noise from PPG or ECG waveforms. Exemplary methods forpre-treatment may include low-pass filtering, band-pass filtering,wavelet transform, median filtering, morphological filtering, curvefitting, Hilbert-Huang transform, or the like, or any combinationthereof. Descriptions regarding methods and systems for reducing orremoving noise from a physiological signal, e.g., a PPG signal or an ECGsignal, may be found in, e.g., International Patent Application Nos.PCT/CN2015/077026 filed Apr. 20, 2015, PCT/CN2015/077025 filed Apr. 20,2015, and PCT/CN2015/079956 filed May 27, 2015, each of which isincorporated by reference.

The pre-treatment unit 610 may include one or more pre-treatmentsub-units (not shown in FIG. 6). The pre-treatment sub-units may (notshown in FIG. 6) perform one or more pre-treatment steps forpre-treating the acquired signals in series (e.g., a pre-treatment stepperformed after another pre-treatment step has commenced or completed)or in parallel (e.g., some pre-treatment steps performed at or aroundthe same time). The pre-treatment unit 610 may control or coordinate theoperations of the pre-treatment sub-units (not shown in FIG. 6). Thecontrol or coordination may be performed by, e.g., a controller withinthe pre-treatment unit 610 (not shown in FIG. 6) or a controller outsideof the pre-treatment unit 610. The pre-treatment sub-units may bearranged in series or in parallel.

This description is intended to be illustrative, and not to limit thescope of the claims. Many alternatives, modifications, and variationswill be apparent to those skilled in the art. The features, structures,methods, and other characteristics of the exemplary embodimentsdescribed herein may be combined in various ways to obtain additionaland/or alternative exemplary embodiments. For example, the pre-treatmentsub-units may be combined variously in order to achieve betterpre-treatment effect. It should be noted that the pre-treatmentsub-units are not necessary for the function of the system. Similarmodifications should fall within the metes and bounds of the claims.

The recognition unit 620 is configured for analyzing the acquiredinformation to recognize or identify a feature. In some embodiments, theacquired information may have been pre-treated before it is processed inthe recognition unit 620. In the exemplary context of blood pressuremonitoring, the acquired information may include a PPG signal, an ECGsignal, a BCG signal, or the like, or a combination thereof; exemplaryfeatures of the acquired information may include waveform,characteristic points, peak points, valley points, amplitude, timeintervals, phase, frequencies, cycles, or the like, or any combinationthereof.

The recognition unit 620 may be configured for analyzing different typesof information or different portions of information. The analysis may beperformed by, e.g., one or more recognition sub-units (not shown in FIG.6). For example, the acquired information includes various types ofphysiological signals (e.g., a PPG signal and an ECG signal) and may beanalyzed by different recognition sub-units. Exemplary methods that maybe employed in the recognition unit 620 may include a threshold method,a syntactic approach of pattern recognition, Gaussian functiondepression, wavelet transform, a QRS complex detection, a lineardiscriminant analysis, a quadratic discriminatory analysis, a decisiontree, a decision table, a near neighbor classification, a wavelet neuralnetworks algorithm, a support vector machine, gene expressionprogramming, hierarchical clustering, a mean cluster analysis, aBayesian network algorithm, a principal component analysis, a Kalmanfilter, Gaussian regression, linear regression, Hidden Markov Model,association rules, an inductive logic method, or the like, or anycombination thereof. Various methods may be used in parallel or may beused in combination. Merely byway of example, the recognition unit mayuse two different methods when processing two types of signals. Asanother example, the recognition unit may use two different methods,e.g., one method after another, when processing one type of signal.

This description is intended to be illustrative, and not to limit thescope of the present disclosure. Many alternatives, modifications, andvariations will be apparent to those skilled in the art. The features,structures, methods, and other characteristics of the exemplaryembodiments described herein may be combined in various ways to obtainadditional and/or alternative exemplary embodiments. Merely by way ofexample, the analyzed features may be uploaded to the public clouds orthe personal clouds and may be used in subsequent calculation orcalibration. As another example, the recognition sub-units (not shown inFIG. 6) are not necessary for the function of the system. Similarmodifications should fall within the metes and bounds of the presentdisclosure.

The calculation unit 630 may be configured for performing variouscalculations to determine, e.g., coefficients of a model or functionrelating to a physiological parameter of interest, the physiologicalparameter of interest, or the like, or a combination thereof. Forinstance, the calculation unit 630 may be configured for calculating,e.g., different coefficients of a model or function relating to aphysiological parameter of interest, different coefficients of differentmodels or functions illustrating the correlation of a physiologicalparameter of interest and one or more measurable signals or otherinformation. The calculation unit 630 may include one or morecalculation sub-units (not shown in FIG. 6) to perform the calculations.A physiological parameter of interest may including, e.g., PTT, PTTV(pulse transit time variation), a BP, a SBP, a DBP, a pulse rate, aheart rate, a HRV, cardiac murmur, blood oxygen saturation, a blooddensity, a blood oxygen level, or the like, or any combination thereof.

Exemplary methods that may be employed in the calculation unit 630 mayinclude a direct mathematical calculation, an indirect mathematicalcalculation, a compensated calculation, a vector operation, a functionoperation, a wave speed evaluation, an equation parameter evaluation, atension evaluation, or the like, or any combination thereof. One or morecalculation models may be integrated in the calculation sub-units, orthe calculation models may be placed in the server 120, or thecalculation models may be placed in public clouds. Different models maybe loaded when different coefficients or physiological parameters are tobe calculated. For example, a linear calculation model in a calculationsub-unit may be used for calculating the SBP, while another non-linearcalculation model in another calculation sub-unit may be used forcalculating the DBP. An initial data or intermediate result used forcalculating a physiological parameter of interest may be retrieved orloaded from the information acquisition module 210, the analysis module220, the server 120, the peripheral equipment 240, or the like, or anycombination thereof. The initial data and the intermediate result may becombined in various ways in the calculation unit 630.

This description is intended to be illustrative, and not to limit thescope of the present disclosure. Many alternatives, modifications, andvariations will be apparent to those skilled in the art. The features,structures, methods, and other characteristics of the exemplaryembodiments described herein may be combined in various ways to obtainadditional and/or alternative exemplary embodiments. In one embodiment,calculated coefficients or calculated physiological parameters may beused as an intermediate result for further analysis. In another example,an individual physiological parameter of interest or one group ofrelated physiological parameters of interest may be calculated by thecalculation unit.

The calibration unit 640 may be configured for performing a calibration.The calibration (also referred to as calibration process or calibrationprocedure) may include one or more steps of retrieving calibration data(or calibration values) for a subject; acquiring a set of information ofthe subject using a device to be calibrated or used in a future process(e.g., a wearable or portable device); determining a calibrated model ora portion thereof for the calibrated device with respect to the subject,or the like, or a combination thereof. The acquired set of informationmay include information provided by the subject or a user other than thesubject, or information acquired by using the device to be calibrated,or the like, or a combination thereof. A set of calibration data mayinclude a specific physiological parameter of interest obtained in onecalibration process, an acquired set of information relating to thespecific physiological parameter of interest in the same calibrationprocess.

Merely by way of example, the device to be calibrated may calculateblood pressure (including the SBP and the DBP) based on PTT derived froman ECG waveform acquired using the device and a corresponding PPGwaveform acquired using the same device or a different device. In someembodiments, the device to be calibrated may be a portion of the systemother than the calibration unit 640. A set of calibration data mayinclude a SBP and a DBP, both measured by a healthcare provider in ahospital setting, and a corresponding ECG waveform and a correspondingPPG waveform acquired using the device to be calibrated. Thecorresponding ECG waveform and the corresponding PPG waveform acquiredusing the device to be calibrated may correspond to the SBP and the DBPmeasured by a healthcare provider. The corresponding ECG waveform andthe corresponding PPG waveform may be acquired using the device to becalibrated at or around the time the SBP and the DBP are measured by ahealthcare provider.

In some embodiments, a set of calibration data may include a SBP, a DBP,and a corresponding ECG waveform and a corresponding PPG waveform, allacquired using the device to be calibrated. For instance, thecalibration unit 640 may include or communicate with a cuff-based bloodpressure monitor (see FIG. 7). The cuff-based blood pressure monitor maybe integrated into the system or device, or a portion thereof (e.g., thecalculation unit, the information acquisition module, or the like). Forinstance, a cuff-based blood pressure monitor, an ECG monitor that mayacquire ECG information, and one or more PPG sensors may be packagedinto a device, or a system, or a portion thereof. The cuff-based bloodpressure monitor may measure a SBP and a DBP at a certain time interval(e.g., 15 min, 30 min, 1 hour, 2 hour, a day, or the like). The set ofcalibration data may be acquired automatically based on a defaultsetting of the system, or preset instructions by the subject or a userother than the subject (also referred to as a third party). Exemplarythird parties may be a doctor, a healthcare worker, a medicalinstitution, a research facility, a peripheral device of the subject ora user well-connected to the subject, or the like. The set ofcalibration data acquired by the calibration unit 640 may be transmittedto the calculation unit 630 or other modules or units in real time. Theset of calibration data may be stored in a storage device disclosedanywhere in the present disclosure or may be stored in the server 120.If needed, the set of calibration data may be loaded from the storagedevice or the server 120 automatically.

One or more sets of calibration data may be used to determinecoefficients of a calibrated model, or some other portion(s) of thecalibrated model. The calibrated model may be used in a future processfor calculating the physiological parameter of interest based on anotherset of information that is acquired using the calibrated device. In afuture process, the calibrated device may acquire a set of informationthat is the same or similar to the set of information acquired for thecalibration. For instance, the other set of information may includeinformation acquired using the same device as that used in thecalibration (e.g., the device including one or more sensors),information of the same type as that acquired in the calibration (e.g.,the age of the subject, the acquisition time during the day, thephysiological or psychological condition of the subject, or the like, ora combination thereof), or the like, or a combination thereof. Thecalibrated model may be used to calculate or estimate the physiologicalparameter of interest accordingly. Exemplary methods that may be used inthe calibration to obtain the calibrated model may include a regressionanalysis, a linear analysis, a functional operation, reconstitution,Fourier transform, Laplace transform, or the like, or a combinationthereof.

In a calibration process, a set of calibration data may include aspecific physiological parameter of interest obtained based on ameasurement using one or more devices other than the device to becalibrated. Merely byway of example, the specific physiologicalparameter of interest may be obtained based on a measurement performedon the subject by the calibration unit 640 (e.g., a cuff-based bloodmonitor). As another example, the specific physiological parameter ofinterest may be obtained based on a measurement performed on the subjectby a healthcare professional in a hospital or a doctor's office. Asanother example, the specific physiological parameter of interest may beobtained based on a measurement performed on the subject by the subjector someone else using a clinical device or a household device. Forinstance, the physiological parameter of interest may be measured usinga device including, e.g., an auscultatory device, an oscillometricdevice, an ECG management device, a PPG management device, or the like,or any combination thereof.

In a calibration process, a set of calibration data may include aspecific physiological parameter of interest previously calculated orestimated by the system or a portion of the system. Merely by way ofexample, the physiological parameter of interest calculated by thesystem based on a set of acquired information and a calibrated functionin the system may be used in a next calibration to update or generate acalibrated model, and the updated calibrated model may be used in thefuture to calculate the physiological parameter of interest (the firstaspect of the calibration process described above). As another example,the physiological parameter of interest calculated by the system basedon a set of acquired information and a calibrated function in the systemmay be used in a next measurement for the physiological parameter ofinterest (the second aspect of the calibration process described above).The calculated physiological parameter of interest of the subject may bestored in a storage device disclosed anywhere in the present disclosureor in the server 120, for future use in connection with the subject orother subjects.

In the exemplary context of estimating BP of a subject (including SBPand DBP), based on PTT, the correlation between BP and PTT may berepresented by a model including mathematical processing, and a factoredfunction, while the factored function may include a function (ƒ) andcoefficient (B). As used herein, a calibration may include at least twoaspects. A first aspect is that a model is determined based on one ormore sets of calibration data (or calibration values). The determinedmodel may be referred to as a calibrated model. To use the calibratedmodel in a specific measurement, signals need to be acquired to providePTT, and a set of calibration data including PTT0, SBP0, and DBP0. Thecorrelation between BP and PTT may depend on other elements, in additionto PTT. Merely by way of example, the correlation between BP and PTT maydepend on HRV, PTTV, in addition to PTT. To use the calibrated model ina specific measurement, signals need to be acquired to provide PTT, HRV,and PTTV, and a set of calibration data including PTT0, SBP0, DBP0,HRV0, and PTTV0.

The first aspect of calibration may be performed using personalizedcalibration data relating to the subject, or peer data, or empiricaldata. This aspect of calibration may be performed real time when aspecific measurement is performed. A model to be used to estimate BPbased on the PTT in the specific measure may be derived based on one ormore sets of calibration data. The selection of the one or more sets ofcalibration data may be based on the PTT in the specific measurement.This aspect of calibration may be perform offline, independent of aspecific measurement.

A second aspect of the calibration includes acquiring a set ofcalibration data to be applied in a calibrated model so that a bloodpressure may be estimated based on PTT acquired in a specificmeasurement, according to the model and the set of calibration data. Insome embodiments, the set of calibration data to be used in the specificmeasurement may be selected from, e.g., a plurality of sets ofcalibration data. The plurality of sets of calibration data may includepersonalized data relating to the subject, peer data, or empirical data.The plurality of sets of calibration data may be saved in the system,e.g., in the library 1100 (see FIG. 1). The plurality of sets ofcalibration data may be saved in a server that is part of or accessiblefrom the system. In some embodiments, the set of calibration data may beselected based on the PTT in the specific measurement.

A calibrated model to be used for a specific subject may be based on thecalibration data of the same subject. A calibrated model to be used fora specific subject may be based on a combination of the calibration dataof the same subject and calibration data from a group of subjects (e.g.,peer data discussed elsewhere in the present disclosure). A calibratedmodel to be used for a specific subject may be based on the calibrationdata from a group of subjects (e.g., peer data or empirical datadiscussed elsewhere in the present disclosure). The specific subject maybe included in the group, or not included. The calibration data may bestored in a storage device disclosed anywhere in the present disclosureor the server 120, or the like, or a combination thereof. Personalizedcalibration data of different subjects may be stored in correspondingpersonal accounts of respective subjects in the server 120 or a personalcloud. Calibration data from various subjects may be stored in anon-personalized database for future use. For instance, calibration datafrom various subjects may be divided based on one or morecharacteristics of the respective subjects. Exemplary characters mayinclude, e.g., age, gender, stature, weight, a body fat percentage,color of skin, a family health history, a life style, an exercise habitor other habit, diet, a psychological condition, a health condition, aneducation history, occupation, or the like, or a combination thereof. Insome embodiments, a portion of the calibration data (e.g., peer datadiscussed elsewhere in the present disclosure) so divided may be usedfor calibration purposes by a group of subjects that share the same orsimilar characteristic(s).

This description is intended to be illustrative, and not to limit thescope of the present disclosure. Many alternatives, modifications, andvariations will be apparent to those skilled in the art. The features,structures, methods, and other characteristics of the exemplaryembodiments described herein may be combined in various ways to obtainadditional and/or alternative exemplary embodiments. For example, astorage unit (not shown in FIG. 6) may be added to the calibration unit640 or the calculation unit 630, or a combination thereof. The storageunit in the calibration unit 640 may store the calibration data orhistorical data relating to calibration process. The storage unitrelating to calculation unit 630 may store calculation algorithms ordata relating to calculation process. Additionally, peer data may beused as initial data or an intermediate result during calibrating.

The analysis module 220 may be implemented on one or more processors.Various units of the analysis module 220 may be implemented on one ormore processors. For example, the pre-treatment unit 610, therecognition unit 620, the calculation unit 630, and the calibration unit640 may be implemented on one or more processors. The one or moreprocessors may transmit signals or data with a storage device (not shownin FIG. 6), the information acquisition modules 1, 2, and 3, theperipheral equipment 240, and the server 120. The one or more processorsmay retrieve or load signals, information, or instructions from thestorage device (not shown in FIG. 6), the information acquisitionmodules 1, 2, and 3, the peripheral equipment 240, or the server 120,and process the signals, information, data, or instructions, or acombination thereof, to perform pre-treatment, calculation of one ormore physiological parameters of interest, calibration, or the like, ora combination thereof. The one or more processors may also be connectedor communicate with other devices relating to the system 100, andtransmit or share signals, information, instructions, the physiologicalparameters of interest, or the like with such other devices via, e.g., amobile phone APP, a local or remote terminal, or the like, or acombination thereof.

FIG. 7 is a block diagram illustrating an architecture of thecalibration unit 640 according to some embodiments of the presentdisclosure. In some embodiments, the calibration unit 640 may beconfigured for acquiring one or more sets of calibration data. Thecalibration unit 640 may perform a calibration process regarding aphysiological parameter of interest, e.g., blood pressure. Thecalibration unit 640 may include, without limitations to, a pneumaticdevice 710, a controller 720, a storage 730, and a transceiver 740.

The pneumatic device 710 may be configured for acquiring a set ofcalibration data (e.g., SBP0, DBP0, or the like, or a combinationthereof.). The pneumatic device 710 may be used to generate compressedair pressure and to receive a set of data based on the variation of theair pressure. In some embodiments, the pneumatic device 710 may includea cuff-based blood pressure monitor. The cuff-based blood pressuremonitor may include a cuff (not shown), a pump (not shown), a valve (notshown), a pressure transducer (not shown), or the like. The cuff-basedblood pressure monitor may inflate the cuff and acquire a set ofcalibration data (e.g., SBP0, DBP0, or the like, or a combinationthereof.). In some embodiments, the cuff may include an air bladderinside. The pump may be used to supply air for the air bladder or may beused to control the speed or the volume of the air supply. The valve maybe used to control ON/OFF of the air supply, inflation or deflation ofthe air bladder in the cuff. The pressure transducer may be used todetect in real time a pressure or a pressure fluctuation, or may be usedto generate an electric signal regarding the pressure or the pressurefluctuation. In some embodiments, the components may be coupled orintegrated in the cuff. In some embodiments, some or all of thecomponents may be integrated in the controller 720.

The controller 720 may be configured for controlling a parameter of aprocess or an operation performed by the calibration unit 640, thepneumatic device 710, or the transceiver 740. The parameter may includean ON/OFF condition, a switching frequency of ON or OFF, a time intervalbetween ON and OFF, a time interval for data acquisition (e.g., 15minutes, 30 minutes, 1 hour, 2 hours, 5 hours, 10 hours, 12 hours, 1day, 2 days, 1 week, 2 weeks, 1 month, 2 months, or the like), or thelike, or a combination thereof. In some embodiments, one or morecontrolling operations in the pneumatic device 710, e.g., the speed orvolume of the air supply, inflation or deflation of the air bladder, orthe like, may be performed by the controller 720. In some embodiments,the controller 720 may control the inflation and deflation of the airbladder according to the pressure detected by the pressure transducer.For instance, when the pressure reaches or exceeds a predetermined value(which may be generally larger than the systolic pressure) and/ormaintains the predetermined value for a predetermined time, thecontroller 720 stops the inflation of the air bladder and then triggersthe deflation of the air bladder. The predetermined value and thepredetermined time may be set by system default, the subject, a userother than the subject (e.g., a doctor), or the like. During thedeflation, the pressure may be recorded, processed and/or analyzed toobtain some calibration data, e.g., the SBP0 and DBP0 value. In someembodiments, the controller 720 may be configured for processing thereceived signals or information, e.g., the conversion of a signal fromone format to another (for example, from an analog signal to a digitalsignal), calculating SBP0 or DBP0 based on the pressure change, or thelike. In some embodiments, the controller 720 may be configured forcoordinating the communication and/or operations of the pneumatic device710, the storage 730, and the transceiver 740. In some embodiments, thecontroller 720 may perform a calibration process to obtain a calibratedmodel that may be used in the calculation process. In some embodiments,the controller 720 may be integrated in the pneumatic device 710.

The storage 730 may be configured for storing data, signal, information,calculation model or calibration model recorded in the calibration unit640, generated by the pneumatic device 710, generated after thecalibration process by the controller 720, or inputted into thecalibration unit 640 from the peripheral equipment 240, the subject, ora user other than the subject. The storage 730 may include a storagedevice disclosed anywhere in the present disclosure, e.g., systemstorage, a storage module or unit integrated in any module or unit ofthe system. The storage 730 may be integrated into any unit described inFIG. 6, or the storage 730 is unnecessary while any storage devicedisclosed anywhere in the present disclosure may be used.

The transceiver 740 may be configured for receiving or transmittingdata, signals, information, or instructions among the pneumatic device710, the controller 720, the storage 730, the peripheral equipment 240or the server 120. One or both of the receiving and transmittingprocedure may be realized through radio technology, RF technology,telephony or Ethernet networks, or the like, or a combination thereof.The receiving and transmitting procedure may be conducted simultaneouslyor asynchronously. In some embodiments, the transceiver 740 may beintegrated in the pneumatic device 710 or the controller 720. Thetransceiver 740 may receive a signal or information from other portionsof the system. For instance, the transceiver 740 may receive a PTT0acquired by the calculation unit 530 of the analysis module 220. ThePTT0 may correspond to blood pressure (including, for example, SBP0 andDBP0) measured by the pneumatic device 710 at or around the acquisitiontime when the signals (for example, an ECG and a PPG) used to derive thePTT0 are acquired. The time to measure the blood pressure (including,for example, SBP0 and DBP0) by the pneumatic device 710 may be at thesame time as the acquisition time. The time to measure the bloodpressure (including, for example, SBP0 and DBP0) by the pneumatic device710 may be less than 1 minute, or 2 minutes, or 3 minutes, or 4 minutes,or 5 minutes, or 8 minutes, or 10 minutes, or 15 minutes, or 20 minutes,or 30 minutes, or 60 minutes apart from the acquisition time.

In some embodiments, the calibration unit 640 may be configured forperforming the calibration process automatically. Merely by way ofexample, the calibration process may be performed at a certain timeinterval. The time interval may be 15 minutes, 30 minutes, 1 hour, 2hours, 5 hours, 10 hours, 12 hours, 1 day, 2 days, 1 week, 2 weeks, 1month, 2 months, or the like. The time interval may be set by systemdefault, the subject, a user other than the subject (e.g., a doctor), orthe like. The calibration process may be performed according to aninstruction provided by, for example, a user other than the subject,remotely. For instance, a healthcare provider at a first location mayprovide an instruction that a calibration process is performed on asubject at a second location away from the first location. For instance,the first location may be a doctor's office, and the second location maybe a patient room or the subject's home, and the calibration unit 640may perform calibration process accordingly. The calibration process maybe performed automatically when a triggering event occurs. For instance,an abnormal blood pressure is acquired by other components of the system(for example, according to the method as illustrated in FIG. 8), acalibration process may be performed automatically. The pump may inflatethe air bladder automatically at the time interval, thus signals orinformation regarding the blood pressure may be acquired automatically.The acquired signals or information may be processed to generate a setof calibration data (e.g., SBP0, DBP0, or the like). The set ofcalibration data may be loaded by the calculation unit 630 and to beused during the calculation process. The measurement of the bloodpressure may be coordinated with the acquisition of signals orinformation for calibration purposes through controller 720 or othermodules or units inside or outside the calibration unit 640.

FIG. 8 is a flowchart diagram of an exemplary process for estimatingblood pressure according to some embodiments of the present disclosure.Beginning in step 810, information including a first signal and a secondsignal may be acquired. The acquisition of the signals may be performedby the information acquisition module 210. In some embodiments, thefirst and second signals may be acquired simultaneously, at or aroundthe same time. In some embodiments, one signal may be acquired prior tothe other signal. In some embodiments, information including or relatingto the first signal or the second signal may be acquired in step 810.For instance, a blood oxygen information may be acquired. As anotherexample, basic information relating to the subject and/or environmentalinformation may be acquired.

Merely by way of example, the first signal or the second signal may bephysiological signals, e.g., an electrocardiogram (ECG) signal, apulse-wave-related signal (such as photoplethysmogram (PPG)), aphonocardiogram (PCG) signal, an impedance cardiogram (ICG) signal, orthe like, or any combination thereof. In some embodiments, the firstsignal and the second signal may be of different types. For example, thefirst and second signals may be the combination of an ECG signal and aPPG signal, the combination of an ECG signal and a PCG signal, thecombination of an ECG signal and an ICG signal, or the like. In someembodiments, the first signal and the second signal may be of the sametype. For example, the first and second signals may be two PPG signalsthat may be detected at different locations on the body of the subject.The exemplary locations on the body of the subject may include, e.g.,the finger, the radial artery, the ear, the wrist, the toe, or thelocations that are more suitable for ambulatory monitoring in currentsensor designs.

In step 820, at least some of the acquired information may bepre-treated. In some embodiments, the acquired first and second signalsmay be pre-treated. The pre-treatment may be performed to reduce orremove noise or interferences in the signals or signal related data.Exemplary methods that may be used in the pre-treatment may includelow-pass filtering, band-pass filtering, wavelet transform, medianfiltering, morphological filtering, curve fitting, Hilbert-Huangtransform, or the like, or any combination thereof. During the processof the pre-treatment, the methods mentioned herein may be used inparallel or may be used in combination. Descriptions regarding methodsand systems for reducing or removing noise from a physiological signal,e.g., a PPG signal or an ECG signal, may be found in, e.g.,International Patent Application Nos. PCT/CN2015/077026 filed Apr. 20,2015, PCT/CN2015/077025 filed Apr. 20, 2015, and PCT/CN2015/079956 filedMay 27, 2015, each of which is incorporated by reference. Additionally,real-time transformation of time domain or frequency domain may also beimplemented in step 820, and the signals or related information may beused in time domain, frequency domain, or both.

In step 830, the features of the first and second signals may berecognized or identified. In the exemplary context of blood pressuremonitoring, the first signal or the second signal may include a PPGsignal, an ECG signal, a BCG signal, or the like; exemplary features ofthe first signal or the second signal may include waveform,characteristic points (or fiducial points), peak points, valley points,amplitude, phase, frequency, cycle, or the like, or any combinationthereof. For example, one characteristic point may be a peak or a valleyof the first signal and a peak or a valley of the second signal, e.g.,the peak of R wave of an ECG signal, a peak or a valley of the PPGsignal, a fastest rising point of a PPG signal, a higher order moment ora higher order derivative of the PPG signal, a pulse area of the PPGsignal, a maximum positive peak of S2 of a PCG signal, or a peak of anICG signal, or the like.

In step 840, a parameter based on the recognized features of the firstand the second signals may be calculated. In some embodiments, the timeinterval between the characteristic points of the first and secondsignals may be calculated. In one example, the time interval between theECG fiducial point (typically the R peak, but may also use the Q/S peak,or even the peak of a P/T wave) and a fiducial point marking the pulsearrival is referred to as the PTT. In another example, the time intervalbetween two pulse wave related signals detected at different locations,e.g., between the carotid and femoral arteries, may be used as the PTT.Further PTTV may be approximated based on a group of determined PTT. HRVmay be determined based a group of A RR. As used herein, A RR refers toa time interval between two adjacent R waves (the maximum point of a QRSwaveform).

A pre-treatment step may be performed to assess an acquired signal (forexample, an ECG signal, a PPG signal, etc.) before one or more featuresof the signal is identified. For instance, an acquired ECG signal may beaccessed before one or more features of the signal is identified. Theassessment may be performed to evaluate whether a valid ECG signal isacquired. The assessment may be performed byway of, for example, apattern recognition process. For instance, the R peak of an acquired ECGsignal may be determined by the pattern recognition process. In someembodiments, the system may identify an abnormal signal or waveform(e.g., an abnormal sinus rhythm R wave, another physiological signal, orthe like) that may be unsuitable for determining PTT; such an abnormalsignal or waveform may be abandoned to avoid to be involved in thesubsequent calculation or analysis. In some embodiments, the acquiredECG signal may be compared with a reference signal to determine whetherthe acquired ECG signal includes an abnormal R wave. The referencesignal may be a normal sinus rhythm ECG signal, or may be retrieved froma database having historical data.

The ECG waveform and the PPG waveform are cyclical signals, i.e. thecharacteristic points occur substantially cyclically or periodically.Thus PTT′ is approximated by a time interval of the maximum point on theQRS complex on the ECG waveform and a peak point on a subsequent(second) PPG waveform. Similarly, PTT″ also may be approximated by atime interval between the peak point on the QRS complex on the ECGwaveform and a peak point on a further (third) PPG waveform. The valueof PTT′ and the value of PTT″ are larger than that of PTT, and errors ordeviations may occur while estimating blood pressure or otherphysiological parameters of interest based on such inaccurate PTT′ andPTT″ values. Such errors or deviations may be avoided or reduced byusing a PPG waveform from the same cycle (driven by the same heart beat)as the ECG waveform. Thus, during recognition of characteristic pointsof the PPG waveform, a threshold may be set regarding the time window orsegment within which the characteristic points on the PPG waveform maybe identified and used to determine PTT. In one example, the time windowmay be 2 seconds or less. Merely by way of example, an analysis toidentify a fiduciary point on a PPG waveform is performed on a segmentof the PPG waveform occurring within 2 seconds from the time point whenthe maximum point on the ECG waveform is identified, in order toapproximate the PTT. As another example, an analysis to identify afiduciary point on a PPG waveform is performed on a segment of the PPGwaveform occurring between two consecutive peak points on the ECGwaveform, in order to approximate the PTT. As a further example, thetime window may be set based on the heart rate of the subject. Forinstance, the time window may be set based on the heart rate of thesubject at or around the acquisition time, or an average heart rate ofthe subject for a period of time, or an average heart rate of a group ofpeople (for example, a sub-group of people who share a same or similarcharacteristic with the subject; exemplary characteristic may includeage, gender, nation, stature, weight, a body fat percentage, color ofskin, a family health history, a life style, an exercise habit or otherhabit, diet, occupation, illness history, education background, maritalstatus, religious belief, or the like, or any combination thereof.

The cycle of ECG or the cycle of PPG may vary. As an example, the cycleof ECG or the cycle of PPG of different subjects may be different. Asanother example, the cycle of ECG or PPG of the same subject may varyunder different situations (e.g., when the subject is exercising orasleep, at different times of a day, at the same or similar time ondifferent days), or the like, or a combination thereof. In one example,the time window threshold may be set based on the heart rate of asubject (for example, the cycle of average person is approximately60-120 beats per minute). The heart rate may be an average value over aperiod of time (e.g., a week, a month, a year, or the like). The heartrate may be one measured at or around the acquisition time. The heartrate may be measured based on, e.g., the ECG signal, the PPG signal, orthe like. The time window may be set or updated based on the measuredheart rate. In another example, the time window may be set by, e.g., thesystem, the subject, or a user other than the subject, based on thephysiological information of the subject. For example, the physiologicalinformation may include motion or not, taking medicine or not, good orbad mood, emotional stress or not, or the like, or a combinationthereof. In another example, the time window may be a fixed valuedefined by the system, the subject, or a user other than the subject(e.g., his doctor, health care provider, or the like).

In step 850, BP (blood pressure) values may be calculated based on thecalculated parameters, e.g., the determined PTT (pulse transit time),PTTV (pulse transit time variation) and HRV, or the like, or acombination thereof. The calculation may be performed based on acalibrated model. The calibrated model may include a linear function ormodel, a nonlinear function or model. The calibration may be performedat step 860. The calibration may be performed periodically, by systemdefault, upon a subject's instruction, or the like. The calibration maybe performed automatically at a certain time interval, by systemdefault, upon a subject's instruction, or the like. The calibration maytake time-varying properties into account. The time-varying propertiesmay include, e.g., the arterial propagation path of a specific subject,the heart movement of a specific subject, the real-time temperature orhumidity, the updated fiducial BP of a specific subject, the updateddatabase storing historical data (SBP/DBP values, BP calculatingalgorithms, etc.) of a specific subject, the updated database storingreference data of people sharing the same or similar characteristics(e.g., age, gender, stature, weight, a body fat percentage, color ofskin, a family health history, a life style, an exercise habit, diet, apsychological condition, a health condition, an education history,occupation, or the like, or any combination thereof), or the like, orany combination thereof.

In some embodiments, the measurement may be performed automaticallyaccording to, for example, a default setting of the system, a presetinstruction by the subject or a user other than the subject (e.g., adoctor). For example, the measurement may be performed at a certain timeinterval, e.g., 15 minutes, 30 minutes, 1 hour, 2 hours, 5 hours, 10hours, 12 hours, a day, two days, a week, two weeks, a month, twomonths, or the like. In another example, the measurement may beperformed in real time. In some embodiments, the measurement may beperformed manually by the subject or a user other than the subject(e.g., a doctor).

While the foregoing has described what are considered to constitute thepresent disclosure and/or other examples, it is understood that variousmodifications may be made thereto and that the subject matter disclosedherein may be implemented in various forms and examples, and that thedisclosure may be applied in numerous applications, only some of whichhave been described herein. Those skilled in the art will recognize thatpresent disclosure are amenable to a variety of modifications and/orenhancements. For example, the pre-treatment step 530 may not benecessary. Additionally, a third signal may be acquired if needed, andthe third signal may be a signal with the same type with the firstsignal or the second signal, or may be a signal different with the firstsignal or the second signal.

FIG. 9-A and FIG. 9-B provide an exemplary process regarding aphysiological parameter monitoring according to some embodiments of thepresent disclosure. Beginning in step 901, a first signal may beacquired. The first signal may be an ECG signal. In some embodiments,information including an ECG signal may be acquired. The first signalmay be acquired by the first acquisition unit 420 in the informationacquisition module 210, or may be acquired by another acquisition unit(not shown), or may be inputted by a subject or a user other than thesubject (e.g., a related third party). The first signal may be acquiredin real time or may be acquired at a certain interval.

In step 902, second information including a plurality of second signalsmay be acquired. In some embodiments, the second information may beblood oxygen information of the subject. The second signal may be a PPGsignal or another pulse wave-related signal. The plurality of secondsignals may be acquired simultaneously or may be acquired one afteranother. The plurality of second signals may be acquired in real time ormay be acquired at a certain time interval. Similarly the secondinformation or the plurality of second signals may be acquired by thesecond acquisition unit 420 in the information acquisition module 210,or may be acquired by another acquisition unit (not shown), or may beinputted by a subject or a user other than the subject (e.g., a relatedthird party).

Merely by way of example, the second information may be blood oxygeninformation of the subject. The second signal may be a PPG signal oranother pulse wave-related signals. During the acquisition of the bloodoxygen information, two PPG signals may be acquired. The two PPG signalsmay be detected from the subject based on light of two differentwavelengths (or within a respective range thereof, e.g., red andinfrared) emitted by one or more emitting ends of one or morephotoelectric sensors. The wavelength may be the visible spectrum, theinfrared region, or the like, or a combination thereof. One or morefeatures of the two PPG signals may be extracted. The extraction may beperformed by the processing unit 540. The blood oxygen level may bedetermined or calculated based on the extracted features.

The first signals and the plurality of second signals may be acquiredsimultaneously or at around a time. The acquired first signals,plurality of second signals, and the determined blood oxygen informationmay be uploaded to the personal health manager 1000 in step 911. Thepersonal health manager 1000 may be described in detail in FIG. 10.

In step 903, one second signal may be selected from the plurality ofsecond signals. The selection may be performed based on a defaultsetting of the system, or an instruction provided by the subject or auser other than the subject (e.g., a related third party), or a presetcondition stored in the system or in the server 120 that may betriggered if applicable, or the like, or a combination thereof. In someembodiments, the preset condition may relate to a parameter of theplurality of second signals, or a parameter regarding an acquisitionprocess of the plurality of second signals. For instance, a secondsignal resulted from a specific emitted light (e.g., a red light) may beselected. In another example, the selection may be performed based onthe intensity of the plurality of second signals, e.g., an intensitythreshold may be set, a signal of which the intensity reaches theintensity threshold may be selected. In a further example, the selectionmay be performed based on the anti-interference ability of the emittedlights, e.g., a signal resulted from a green light of which theanti-interference ability is good may be selected. In some embodiments,the preset condition may relate to a time parameter (e.g., morning,noon, night, or the like), an environmental parameter (e.g.,temperature, humidity, air pressure, or the like), a physical condition(e.g., motion or not, taking medicine or not, good or bad mood,emotional stress or not, or the like). The selection may be performedautomatically, or may be performed artificially. In some embodiments,the second signal may be a PPG signal. A PPG signal resulted from agreen light having a good anti-interference ability may be selected forsubsequent process.

In step 904, a parameter based on the first signal and the selectedsecond signal may be determined. In some embodiments, the parameter maybe the pulse transit time (PTT). See, for example, FIG. 8. In someembodiments, a parameter such as PTTV, HRV, also may be determined. Instep 905, one or more favorite models may be retrieved based on thesubject's personal data, universal data, additional information in ahistory, or the like, or a combination thereof. As used herein, afavorite model may refer to a model that may provide a more accurateestimate of a physiological parameter of interest from acquiredinformation than one or more other models. The information may beacquired from the server 120, or may be measured by a variety ofsensors. The sensors may be part of the system 100, or communicate withthe system 100. Exemplary sensors may include an accelerometer that maymeasure the movement conditions of a subject during a measurement, aheart rate sensor that may measure a subject's heart rate during ameasurement, a GPS receiver or location sensor that may measure thegeographic location where a measurement occurs or the subject islocated, a temperature sensor that may measure the environmenttemperature and/or the body temperature of a subject at or around anacquisition time, a humidity sensor that may measure the environmenthumidity at or around an acquisition time, or the like, or a combinationthereof. The retrieved favorite model(s) may be used to estimate bloodpressure based on the acquired signals or information.

In step 906, the system may proceed to determine whether calibrationdata is available. As used herein, the calibration data may includeSBP0, DBP0, PTT0, PTTV0, HRV0, or the like, or a combination thereof.The calibration data may be acquired by the calibration unit 640, or maybe acquired by a healthcare provider, or may be acquired by theperipheral equipment 240, or the like. The calibration data may bestored in the calibration unit 640, or the server 120, or any storagedevice disclosed anywhere in the present disclosure. If the answer is“No,” it may follow at least some steps starting from node A 909 asillustrated in FIG. 9-B. If the answer is “Yes,” a set of calibrationdata may be selected in step 907. The selection may be performed basedon a default setting of the system, or instructions inputted by thesubject or a user other than the subject (e.g., a related third party),or a preset condition as described above. In some embodiments, thepreset condition may be a time parameter that may be expressed as, forexample, a time interval within which the calibration data is acquired.Merely by way of example, the time interval may be 15 minutes, 20minutes, 30 minutes, 40 minutes, 1 hour, 2 hours, 5 hours, 10 hours, 12hours, a day, two days, a week, two weeks, a month, two months, or thelike. For instance, if the time interval is set as 15 minutes, a set ofcalibration data acquired within the nearest 15 minutes may be selectedto be used in the estimation of SBP and DBP. In some embodiments, thepreset condition may be a condition regarding a physical conditiondetermination process. The physical condition may include motion or not,sleep or not, whether taking medicine or not, or the like. As usedherein, one physical condition may correspond to one type of calibrationdata. Merely by way of example, if the subject is in motion, thephysical condition may be different with that of in resting state. Inthis case, a set of calibration data that is acquired under the same orsimilar physical condition may be selected. The physiological parametersof the subject may vary with the change of the physical condition. Thuswhile the subject is in a specific physical condition, a correspondingset of calibration data may be used to be involved in subsequentcalculation of physiological parameters.

After the set of calibration data is selected in step 907, the systemmay proceed to step 908 to calculate a physiological parameter ofinterest. In some embodiments, a blood pressure value of the subject maybe calculated based on the determined parameter in step 904 (e.g., PTT,PTTV, HRV, or the like), the selected favorite model(s) in step 905 andthe selected set of calibration data in step 907. More detaileddescriptions regarding the calculation of the blood pressure may befound in International Application Nos. PCT/CN2015/083334 filed Jul. 3,2015 and PCT/CN/2015/096498 filed Dec. 5, 2015. The calculatedphysiological parameter of interest may be uploaded to personal healthmanager 1000 in step 910. The personal health manager may be describedin detail in FIG. 10. In some embodiments, after the physiologicalparameter of interest is calculated, it may proceed to node B 910 asillustrated in FIG. 9-B.

FIG. 9-B illustrates the process starting from node A 909 regardingphysiological parameter calculating or processing according to someembodiments of the present disclosure. If no calibration data isavailable, the system may proceed to step 912 to determine whether touse system default. If the answer is “Yes,” a physiological parameter ofinterest may be calculated based on a default setting of the system instep 914. In some embodiments, a blood pressure value may be calculated.The default setting of the system may include a set of calibration data,for instance, (SBP0=120 mmHg, DBP0=80 mmHg, or the like). In someembodiments, the system may determine whether to use peer data (notshown in FIG. 9). As used herein, the peer group is defined as a groupof people sharing at least some same or similar characteristics, e.g.,same gender, similar age, similar height, similar weight, similar armlength, similar illness history, or the like, or a combination thereof.It should be noted that, other than the peer data, the empirical datamay be acquired by statistical analysis based on data of a group ofsubjects which is not limited to a peer group. The peer data may be usedas calibration data in step 914.

If the answer is “No,” the system may proceed to step 913 to acquire aset of calibration data. The calibration data may include SBP0, DBP0,PTT0, PTTV0, HRV0, or the like, or a combination thereof. The set ofcalibration data may be acquired by the calibration unit 640, or may beacquired by a healthcare provider, or may be acquired by the peripheralequipment 240, or the like. The acquisition of the set of calibrationdata may be triggered automatically or artificially. In someembodiments, the calibration data may be acquired automatically at acertain time interval (e.g., 15 minutes, 30 minutes, or the like). Insome embodiments, while the system detect that there is no calibrationdata available, an operator (e.g., a doctor) may manually turn on acalibration data acquisition process. Then a physiological parameter ofinterest (e.g., blood pressure) may be calculated based on the acquiredset of calibration data in step 914. The physiological parameter ofinterest may be uploaded to personal health manager 1000 illustrated inFIG. 10.

In step 915, whether to perform a comparison with historical data and/orpeer data may be chosen. The determination may be made by the system ora portion thereof (for example, based on an instruction provided by asubject, a user other than the subject, a third party, or an instructionor a rule derived by machine learning of prior data, prior behaviors ofthe subject, or of a user other than the subject), or provided by thesubject or by a user other than the subject. If the comparison is notchosen, the calculated physiological parameter of interest may beuploaded to the personal health manager 1000 in step 911. If thecomparison is chosen, relation information with historical data and/orpeer data may be generated in step 910. The relation information may bestored in the server 120, the analysis module 220, or any storage devicedisclosed anywhere in the present disclosure. The form of the relationinformation may be text, a graph, a three dimensional image, a code, avoice message, video, an audio alert, a haptic effect, or the like, or acombination thereof. The relation information may be uploaded to thepersonal health manager 1000 in step 911. Merely by way of example, avariation curve with historical data may be generated. The variationcurve may be displayed in real time in the personal health manager 1000.

This description is intended to be illustrative, and not to limit thescope of the present disclosure. Many alternatives, modifications, andvariations will be apparent to those skilled in the art. The features,structures, methods, and other characteristics of the exemplaryembodiments described herein may be combined in various ways to obtainadditional and/or alternative exemplary embodiments. For example, anyacquired signal, information or data may be uploaded to the server 120.Any determined or calculated data or inter-data may be uploaded to theserver 120.

FIG. 10 is an example of the composition and organization of thepersonal health manager 1000 according to some embodiments of thepresent disclosure. The personal health manager 1000 may be stored inthe server 120, locally on a measuring device 110, a terminal 140connected or communicated with the system, or the like, or a combinationthereof. For instance, the personal health manager 1000 may be stored inthe system. As another example, the personal health manager 1000 may betransmitted to a related third party (e.g., a hospital). The personalhealth manager 1000 may be displayed on a terminal 140, a display deviceof the system (not shown), a display device of a third party, or thelike, or a combination thereof. The personal health manager 1000 may beintegrated as a portion of the system, or may be configured as aperipheral device connected or communicated with the system.

As illustrated in FIG. 10, the personal health manager 1000 may includea plurality of sections showing a plurality of physiological parametersof interest or physiological signals or information of the subject. Thesections may include cardiac information (e.g., ECG, heart rate, etc.),blood oxygen related information (e.g., Spo2, pulse rate, PPG, etc.),blood pressure related information (e.g., SBP, DBP, calibration data(e.g., SBP0, DBP0, etc.), etc.). The acquired physiological signals,determined parameters and calculated physiological parameters ofinterest may be displayed on the personal health manager 1000 in realtime. The subject or a user other than the subject may browse thesections to review information relating to the acquired signals,parameters and information at any time by choosing any time point (e.g.,5:00 am, 9:00 am, 10:00 am, 12:00 am, 15:00 pm, 20:00 pm, or the like)or anytime interval (e.g., 15 minutes, 30 minutes, 1 hour, 2 hours, 5hours, 10 hours, 20 hours, 1 day, two days, a week, two weeks, a month,two months, or the like).

In some embodiments, the personal health manager 1000 may include otherrelevant information including, for example, personal information (e.g.,age, gender, height, weight, illness history, or the like, or acombination thereof.) and external environmental information (e.g.,temperature, humidity, air quality, ultraviolet intensity, or the like,or a combination thereof).

In some embodiments, the personal health manager 1000 may include otherrelevant information including, for example, reference information (notshown in FIG. 10) for a related third party. The related third party maybe a doctor, a healthcare worker, a medical institution, a researchfacility, or the like, or a combination thereof. The referenceinformation may be generated based on the status of the calculatedphysiological parameters of interest, the basic information of thesubject, or both. The reference information may be generated based onthe relation information generated in step 910. The referenceinformation may include information regarding determination of surgeryspot, or whether needs anesthesia, or type and dose of drugs, or thelike, or a combination thereof. The reference information may providesome guidance to the related third party at a right moment.

In some embodiments, the personal health manager 1000 may include otherrelevant information including, for example, health tips (not shown inFIG. 10) regarding sleep, diet, exercises, or the like, or a combinationthereof. The health tips may be retrieved from the server 120 based onthe basic information of the subject, the calculated physiologicalparameters of interest, the variations, or the like, or a combinationthereof. A related third party (for example, a doctor, a healthcareworker, a medical institution, a research facility, a peripheral deviceof the subject or a user well-connected to the subject, or the like) mayinput some health tips in the personal health manager. Similarly, thesubject or a user other than the subject also may input a memo listregarding sleep, diet, exercises, or the like. The subject may customizean information push regarding health tips based on an option or aninstruction. More detailed descriptions regarding the personal healthmanager may be found in International Application No PCT/CN/2015/096498filed Dec. 5, 2015.

FIG. 11 depicts the architecture of a mobile device that may be used torealize a specialized system implementing the present disclosure. Inthis example, the device (for example, the terminal 140) on whichinformation relating to blood pressure monitoring is presented andinteracted-with is a mobile device 1100, including, but is not limitedto, a smart phone, a tablet, a music player, a handled gaming console, aglobal positioning system (GPS) receiver, and a wearable computingdevice (for example, eyeglasses, wrist watch, etc.), or in any otherform factor. The mobile device 1100 in this example includes one or morecentral processing units (CPUs) 1140, one or more graphic processingunits (GPUs) 1130, a display 1120, a memory 1160, a communicationplatform 1110, such as a wireless communication module, storage 1190,and one or more input/output (I/O) devices 1150. Any other suitablecomponent, including a system bus or a controller (not shown), may alsobe included in the mobile device 1100. As shown in FIG. 11, a mobileoperating system 1170, for example, iOS, Android, Windows Phone, etc.,and one or more applications 1180 may be loaded into the memory 1160from the storage 1190 in order to be executed by the CPU 1140. Theapplications 1180 may include a browser or any other suitable mobileapps for receiving and rendering information relating to blood pressuremonitoring or other information from the engine 200 on the mobile device1100. User interactions with the information stream may be achieved viathe I/O devices 1150 and provided to the engine 200 and/or othercomponents of system 100, for example, via the network 150.

To implement various modules, units, and their functionalities describedin the present disclosure, computer hardware platforms may be used asthe hardware platform(s) for one or more of the elements describedherein (for example, the engine 200, and/or other components of thesystem 100 described with respect to FIGS. 1-10 and 13). The hardwareelements, operating systems and programming languages of such computersare conventional in nature, and it is presumed that those skilled in theart are adequately familiar therewith to adapt those technologies to theblood pressure monitoring as described herein. A computer with userinterface elements may be used to implement a personal computer (PC) orother type of work station or terminal device, although a computer mayalso act as a server if appropriately programmed. It is believed thatthose skilled in the art are familiar with the structure, programmingand general operation of such computer equipment and as a result thedrawings should be self-explanatory.

FIG. 12 depicts the architecture of a computing device that may be usedto realize a specialized system implementing the present disclosure.Such a specialized system incorporating the present teaching has afunctional block diagram illustration of a hardware platform thatincludes user interface elements. The computer may be a general purposecomputer or a special purpose computer. Both may be used to implement aspecialized system for the present disclosure. This computer 1200 may beused to implement any component of the blood pressure monitoring asdescribed herein. For example, the engine 200, etc., may be implementedon a computer such as computer 1200, via its hardware, software program,firmware, or a combination thereof. Although only one such computer isshown, for convenience, the computer functions relating to the bloodpressure monitoring as described herein may be implemented in adistributed fashion on a number of similar platforms, to distribute theprocessing load.

The computer 1200, for example, includes COM ports 1250 connected to andfrom a network connected thereto to facilitate data communications. Thecomputer 1200 also includes a central processing unit (CPU) 1220, in theform of one or more processors, for executing program instructions. Theexemplary computer platform includes an internal communication bus 1210,program storage and data storage of different forms, for example, disk1270, read only memory (ROM) 1230, or random access memory (RAM) 1240,for various data files to be processed and/or transmitted by thecomputer, as well as possibly program instructions to be executed by theCPU. The computer 1200 also includes an I/O component 1260, supportinginput/output between the computer and other components therein such asuser interface elements 1280. The computer 1200 may also receiveprogramming and data via network communications.

Hence, aspects of the methods of the blood pressure monitoring and/orother processes, as outlined above, may be embodied in programming.Program aspects of the technology may be thought of as “products” or“articles of manufacture” typically in the form of executable codeand/or associated data that is carried on or embodied in a type ofmachine readable medium. Tangible non-transitory “storage” type mediainclude any or all of the memory or other storage for the computers,processors, or the like, or associated modules thereof, such as varioussemiconductor memories, tape drives, disk drives and the like, which mayprovide storage at any time for the software programming.

All or portions of the software may at times be communicated through anetwork such as the Internet or various other telecommunicationnetworks. Such communications, for example, may enable loading of thesoftware from one computer or processor into another, for example, froma management server or host computer of the engine 200 into the hardwareplatform(s) of a computing environment or other system implementing acomputing environment or similar functionalities in connection with theblood pressure monitoring. Thus, another type of media that may bear thesoftware elements includes optical, electrical and electromagneticwaves, such as used across physical interfaces between local devices,through wired and optical landline networks and over various air-links.The physical elements that carry such waves, such as wired or wirelesslinks, optical links or the like, also may be considered as mediabearing the software. As used herein, unless restricted to tangible“storage” media, terms such as computer or machine “readable medium”refer to any medium that participates in providing instructions to aprocessor for execution.

Hence, a machine-readable medium may take many forms, including atangible storage medium, a carrier wave medium or physical transmissionmedium. Non-volatile storage media include, for example, optical ormagnetic disks, such as any of the storage devices in any computer(s) orthe like, which may be used to implement the system or any of itscomponents as shown in the drawings. Volatile storage media includedynamic memory, such as a main memory of such a computer platform.Tangible transmission media include coaxial cables; copper wire andfiber optics, including the wires that form a bus within a computersystem. Carrier-wave transmission media may take the form of electric orelectromagnetic signals, or acoustic or light waves such as thosegenerated during radio frequency (RF) and infrared (IR) datacommunications. Common forms of computer-readable media thereforeinclude for example: a floppy disk, a flexible disk, hard disk, magnetictape, any other magnetic medium, a CD-ROM, DVD or DVD-ROM, any otheroptical medium, punch cards paper tape, any other physical storagemedium with patterns of holes, a RAM, a PROM and EPROM, a FLASH-EPROM,any other memory chip or cartridge, a carrier wave transporting data orinstructions, cables or links transporting such a carrier wave, or anyother medium from which a computer may read programming code and/ordata. Many of these forms of computer readable media may be involved incarrying one or more sequences of one or more instructions to a physicalprocessor for execution.

Those skilled in the art will recognize that the present disclosure areamenable to a variety of modifications and/or enhancements. For example,although the implementation of various components described above may beembodied in a hardware device, it may also be implemented as a softwareonly solution—for example, an installation on an existing server. Inaddition, the blood pressure monitoring system as disclosed herein maybe implemented as a firmware, firmware/software combination,firmware/hardware combination, or a hardware/firmware/softwarecombination.

Example

The following example is provided for illustration purposes, and notintended to limit the scope of the present disclosure.

A system used for measuring one or more physiological parameters ofinterest may include a testing device 1300, a peripheral equipment 240(not shown in FIG. 13, see FIG. 2) and a server 120 (not shown in FIG.13, see FIG. 1). FIG. 13 illustrates an exemplary testing device 1300according to some embodiments of the present disclosure. The testingdevice 1300 may include a measurement module 1310, a calibration module1340, and/or a terminal 1360. The testing device 1300 may be connectedor otherwise communicate with the terminal 1360.

The measurement module 1310 may be configured for acquiring information,for example, an ECG signal, a PPG signal, blood oxygen information, orthe like, or a combination thereof. The measurement module 1310 also maybe configured for analyzing and processing the acquired information, ordetermining or estimating a physiological parameter of interest (forexample, a blood pressure), or both. The calibration module 1340 isconfigured for acquiring a set of calibration data. The set ofcalibration data may be transmitted to the measurement module 1310 inreal time. In some embodiments, the set of calibration data may betransmitted to the server 120, or may be displayed in the personalhealth manager 1000. In the embodiment, the calibration module 1340 maybe a cuff-based blood pressure device (e.g., a cuff-based blood pressuremonitor).

According to the embodiment, the measurement module 1310 includes an ECGacquisition unit configured for acquiring ECG signals by way of electricsensing method, and a blood oxygen acquisition unit configured foracquiring blood oxygen related information (e.g., blood oxygen data, aplurality of PPG signals) by way of photoelectric sensing method. Theacquired signals or information may be stored in the server 120, or astorage device (not shown in FIG. 13) integrated in the measurementmodule 1310, or any storage device disclosed anywhere in the presentdisclosure.

The testing device 1300 may be a wearable device, a portable device, amedical monitoring device in hospital, or health-care monitoring deviceat home, or the like. The testing device 1300 may be a monitoringdevice, the schematic diagram is illustrated in FIG. 13 (some detailshave been elided for brevity). The measurement module 1310 includes anECG acquisition unit and a blood oxygen acquisition unit. It may be seenthat a plurality of electrodes 1320 are located on the chest of thesubject and the electrodes are configured for recording one or morepotential changes of the subject. The potential changes may constitutean ECG waveform and the ECG waveform may be transmitted to themeasurement module 1310 by one or more wires. It also may be seen thatone or more photoelectric sensors 1330 are located on the finger of thesubject and the photoelectric sensors are configured for detecting oneor more PPG signals or pulse wave related signals. The detected signalsmay be 10 transmitted to the measurement module 1310 by wires orwirelessly. In this embodiment, the one or more photoelectric sensorsare located on the finger of the subject and this arrangement orlocating form is only provided for illustration purposes. In oneexample, the one or more photoelectric sensors may be located in theupper arm of the subject.

According to the embodiment, the calibration module 1340 may include acuff-based blood pressure monitor. The cuff-based blood pressure monitormay be configured for acquiring SBP and DBP values that may be used ascalibration data (e.g., SBP0, DBP0, PTT0, or the like, or a combinationthereof.) during one or more processes of the measurement module 1310.As illustrated, the cuff-based blood pressure 20 monitor may include acuff (see 1340), a pneumatic device (not shown in FIG. 13), a cable1350, a transceiver (not shown in FIG. 13), and/or a controller (notshown in FIG. 13). The cuff may feature an internal, airtight pocketthat may be secured onto a portion of a subject to deliver a pressure.For instance, the cuff may wrap around the subject's upper arm todeliver a pressure. The pneumatic device may include a pump, a valve,analog/digital converter, etc. During the process of acquiringcalibration data, the pneumatic device may inflate the cuff and acquirea plurality of data (e.g., SBP0, DBP0, or the like, or a combinationthereof.). The acquired data may be transmitted by the cable 1350 to thetransceiver (not shown in FIG. 13) for subsequent process.

The acquired ECG signal, PPG signal, calibration data (e.g., SBP0, DBP0,PTT0, or the like, or a combination thereof) may be transmitted to themeasurement module 1310 to be used for calculating a blood pressurevalue of the subject. The calculation may be performed by themeasurement module 1310, or may be performed by an analysis module (notshown) integrated in the measurement module 1310. In some embodiments,the measurement module 1310 may be a wearable or portable deviceseparate from and capable of communicating with one or morephotoelectric sensors 1330, the electrodes 1320, and/or the calibrationmodule 1340, as illustrated in FIG. 13. In some embodiments, themeasurement module 1310 may be packaged together with the calibrationmodule 1340. For instance, the measurement module 1310 may be attachedto the cuff of the calibration module 1340.

Before the calculation, one or more operations may be performed, forexample, pre-treatment, feature identification, parameter estimation,calibration, or the like, or a combination thereof. More descriptionsregarding the analysis may be found in International Patent ApplicationNo. PCT/CN2015/083334 filed Jul. 3, 2015 and International PatentApplication No. PCT/CN/2015/096498 filed Dec. 5, 2015. The calculatedphysiological parameter of interest may be uploaded to personal healthmanager 1000 as illustrated in FIG. 10. The details may be displayed inthe terminal 1360, or may be transmitted to a related third party (forexample, a medical institution). The details may be displayed in adisplay device (see FIG. 13) of the measurement module 1310.

The testing device 1300 may also include one or more additionalcomponents including a WIFI device, a blue tooth device, a NFC device, aGPS device, or the like, or a combination thereof. For instance, theWIFI device may be used for linking to a wireless network. The bluetooth device may be used for data transformation among some wired orwireless terminals within a certain distance. The NFC device may be usedto enable terminals establishing radio communication within a shortdistance (10 cm or less). The GPS device may allow the subject to findhis own position, or the GPS device may be used to navigate, or thelike, or a combination thereof. The additional components may beconnected or otherwise communicate with the measurement module 1310, thecalibration module 1340, the terminal 1360, and the server 120.

The testing device 1300 may be used in a health care institute (e.g., ahospital), or may be used at home. The testing device 1300 may be usedfor real time physiological parameter monitoring. The acquired signals,information, data, or calculated physiological parameters of interestmay be displayed in real time in a display device (not shown) or in theterminal 1360. The subject, a user other than the subject (e.g., adoctor) may review the related information anywhere and anytime. In someembodiments, if the testing device 1300 is used at home, the testingdevice 1300 may communicate with a healthcare provider located in alocation remote from the subject. The communication may be achieveddirectly by the testing device 1300, or indirectly via, for example, theterminal 1360 carried by the subject. The physiological parameter, aswell as location information, of the subject may be transmitted to thehealthcare provider in real-time, periodically, or when a triggeringevent occurs. Exemplary trigger events are described elsewhere in thepresent disclosure. When an emergency occurs, for example, thephysiological parameter exceeding a threshold, the healthcare providermay be notified, the subject may be located based on the positioninginformation from the GPS or location sensor, and medical services may beprovided accordingly.

Having thus described the basic concepts, it may be rather apparent tothose skilled in the art after reading this detailed disclosure that theforegoing detailed disclosure is intended to be presented by way ofexample only and is not limiting. Various alterations, improvements, andmodifications may occur and are intended to those skilled in the art,though not expressly stated herein. These alterations, improvements, andmodifications are intended to be suggested by this disclosure, and arewithin the spirit and scope of the exemplary embodiments of thisdisclosure.

Moreover, certain terminology has been used to describe embodiments ofthe present disclosure. For example, the terms “one embodiment,” “anembodiment,” and/or “some embodiments” mean that a particular feature,structure or characteristic described in connection with the embodimentis included in at least one embodiment of the present disclosure.Therefore, it is emphasized and should be appreciated that two or morereferences to “an embodiment” or “one embodiment” or “an alternativeembodiment” in various portions of this specification are notnecessarily all referring to the same embodiment. Furthermore, theparticular features, structures or characteristics may be combined assuitable in one or more embodiments of the present disclosure. Inaddition, the term “logic” is representative of hardware, firmware,software (or any combination thereof) to perform one or more functions.For instance, examples of “hardware” include, but are not limited to, anintegrated circuit, a finite state machine, or even combinatorial logic.The integrated circuit may take the form of a processor such as amicroprocessor, an application specific integrated circuit, a digitalsignal processor, a micro-controller, or the like.

Further, it will be appreciated by one skilled in the art, aspects ofthe present disclosure may be illustrated and described herein in any ofa number of patentable classes or context including any new and usefulprocess, machine, manufacture, or composition of matter, or any new anduseful improvement thereof. Accordingly, aspects of the presentdisclosure may be implemented entirely hardware, entirely software(including firmware, resident software, micro-code, etc.) or combiningsoftware and hardware implementation that may all generally be referredto herein as a “circuit,” “unit,” “module,” “component,” or “system.”Furthermore, aspects of the present disclosure may take the form of acomputer program product embodied in one or more computer readable mediahaving computer readable program code embodied thereon.

A computer readable signal medium may include a propagated data signalwith computer readable program code embodied therein, for example, inbaseband or as part of a carrier wave. Such a propagated signal may takeany of a variety of forms, including electro-magnetic, optical, or anysuitable combination thereof. A computer readable signal medium may beany computer readable medium that is not a computer readable storagemedium and that may communicate, propagate, or transport a program foruse by or in connection with an instruction execution system, apparatus,or device. Program code embodied on a computer readable signal mediummay be transmitted using any appropriate medium, including wireless,wireline, optical fiber cable, RF, etc., or any suitable combination ofthe foregoing.

Computer program code for carrying out operations for aspects of thepresent disclosure may be written in any combination of one or moreprogramming languages, including an object oriented programming languagesuch as Java, Scala, Smalltalk, Eiffel, JADE, Emerald, C++, C#, VB. NET,Python or the like, conventional procedural programming languages, suchas the “C” programming language, Visual Basic, Fortran 2003, Perl, COBOL2002, PHP, ABAP, dynamic programming languages such as Python, Ruby andGroovy, or other programming languages. The program code may executeentirely on the user's computer, partly on the user's computer, as astand-alone software package, partly on the user's computer and partlyon a remote computer or entirely on the remote computer or server. Inthe latter scenario, the remote computer may be connected to the user'scomputer through any type of network, including a local area network(LAN) or a wide area network (WAN), or the connection may be made to anexternal computer (for example, through the Internet using an InternetService Provider) or in a cloud computing environment or offered as aservice such as a Software as a Service (SaaS).

Furthermore, the recited order of processing elements or sequences, orthe use of numbers, letters, or other designations therefore, is notintended to limit the claimed processes and methods to any order exceptas may be specified in the claims. Although the above disclosurediscusses through various examples what is currently considered to be avariety of useful embodiments of the disclosure, it is to be understoodthat such detail is solely for that purpose, and that the appendedclaims are not limited to the disclosed embodiments, but, on thecontrary, are intended to cover modifications and equivalentarrangements that are within the spirit and scope of the disclosedembodiments. For example, although the implementation of variouscomponents described above may be embodied in a hardware device, it mayalso be implemented as a software only solution—for example, aninstallation on an existing server or mobile device. In addition, thefinancial management system disclosed herein may be implemented as afirmware, firmware/software combination, firmware/hardware combination,or a hardware/firmware/software combination.

Similarly, it should be appreciated that in the foregoing description ofembodiments of the present disclosure, various features are sometimesgrouped together in a single embodiment, figure, or description thereoffor the purpose of streamlining the disclosure aiding in theunderstanding of one or more of the various inventive embodiments. Thismethod of disclosure, however, is not to be interpreted as reflecting anintention that the claimed subject matter requires more features thanare expressly recited in each claim. Rather, inventive embodiments liein less than all features of a single foregoing disclosed embodiment.

1. A device comprising memory storing instructions; and at least oneprocessor that executes the instructions to perform operationscomprising: receiving a first signal representing a pulse wave relatingto heart activity of a subject, receiving a plurality of second signalsrepresenting time-varying information on a pulse wave of the subject;identifying a first feature in the first signal; identifying a secondfeature in one of the plurality of second signals; and calculating aphysiological parameter of the subject based on a difference between thefirst feature and the second feature.
 2. The device of claim 1, thereceiving the first signal comprising communicating with a first sensorconfigured to acquire the first signal of the subject.
 3. The device ofclaim 1, the receiving the plurality of second signals comprisingcommunicating with one or more second sensors.
 4. The device of claim 2,the first sensor comprising a plurality of electrodes.
 5. The device ofclaim 3, one of the one or more second sensors comprising aphotoelectric sensor.
 6. The device of claim 1, the first signal or thesecond signal comprising an optical signal or an electric signal.
 7. Thedevice of claim 1, wherein the first feature of the first signalcorresponds to a first time point; the identifying the second featurecomprises: selecting a segment of the second signal, the segmentoccurring within a time window from the first time point; and locatingthe second feature corresponding to a second time point in the segment;and the computing the pulse transit time comprises determining a timeinterval between the first time point and the second time point.
 8. Thedevice of claim 1, wherein the first signal or the second signalcomprises an ECG waveform, a PPG waveform, or a BCG waveform.
 9. Thedevice of claim 1 further comprising or configured to communicate with acuff-based blood pressure monitor.
 10. The device of claim 9, thecuff-based blood pressure monitor being configured to coordinate a bloodpressure measurement with the receiving of the first signal or thereceiving of the plurality of second signals. 11-24. (canceled)
 25. Adevice comprising: memory storing instructions; and at least oneprocessor that executes the instructions to perform operationscomprising: receiving a first signal representing a pulse wave relatingto heart activity of a subject, receiving a plurality of second signalsrepresenting time-varying information on a pulse wave of the subject;determining, based on the first signal and at least one of the pluralityof second signals, one or more parameters associated with at least oneof the first signal or the at least one of the plurality of secondsignals; obtaining a calibrated model providing a correlation betweenthe physiological parameter and the one or more parameters; anddetermining, based on the calibrated model and the one or moreparameters, a physiological parameter of the subject.
 26. The system ofclaim 25, wherein the determining, based on the first signal and atleast one of the plurality of second signals, one or more parametersincludes: identifying a first feature in the first signal; identifying asecond feature in the at least one of the plurality of second signals;and determining the one or more parameters based on the first featureand the second feature.
 27. The device of claim 26, wherein the firstfeature of the first signal corresponds to a first time point; theidentifying the second feature comprises: selecting a segment of thesecond signal, the segment occurring within a time window from the firsttime point; and locating the second feature corresponding to a secondtime point in the segment; and the determining the one or moreparameters based on the first feature and the second feature comprisesdetermining a time interval between the first time point and the secondtime point.
 28. The system of claim 25, wherein the determining, basedon the first signal and at least one of the plurality of second signals,a parameter includes: selecting the at least one of the plurality ofsecond signals from the plurality of second signals based on a presetcondition relating to a parameter of each of the plurality of the secondsignals and a parameter regarding an acquisition process of theplurality of second signals.
 29. The system of claim 25, wherein thedetermining, based on the calibrated model and the one or moreparameters, a physiological parameter of the subject includes: obtaininga set of calibration data including a specific physiological parameterobtained based on a previous measurement using one or more calibrationdevices that are different from one or more devices acquiring the firstsignal and the plurality of second signals; and determining, based onthe set of calibration data, the calibrated model, and the one or moreparameters, the physiological parameter of the subject.
 30. The systemof claim 29, wherein the first signal or the at least one of theplurality of second signals comprises an ECG waveform, a PPG waveform,or a BCG waveform, and the specific physiological parameter includes ablood pressure.
 31. The system of claim 30, wherein the one or morecalibration devices that are different from the one or more devicesacquiring the first signal and the plurality of second signals include acuff-based blood pressure device.
 32. The system of claim 25, whereinthe calibrated model is derived by a process including: obtaining one ormore sets of calibration data associated with at least one of thesubject or one or more users other than the subject; obtaining a set ofinformation acquired by one or more devices acquiring the first signaland the plurality of second signals, the set of information includinginformation provided by the subject or the one or more users other thanthe subject, or information acquired by using the one or more devices;and determining, based on the one or more sets of calibration data andthe set of information, the calibrated model.
 33. The system of claim 32wherein the obtaining a calibrated model providing a correlation betweenthe physiological parameter and the one or more parameters includes:retrieving, based on personal data of the subject, the calibrated modelfrom a storage device, the personal data of the subject including atleast one of a movement condition, a location, an environmenttemperature, an environment humidity, or a body temperature of thesubject.
 34. A device comprising: a first sensor configured to acquirereceiving a first signal representing a pulse wave relating to heartactivity of a subject, a second sensor configured to receive a pluralityof second signals representing time-varying information on a pulse waveof the subject; a calibration device configured to obtain a set ofcalibration data; and a processor configured to: determine, based on thefirst signal and at least one of the plurality of second signals, one ormore parameters associated with at least one of the first signal or theat least one of the plurality of second signals; and determining, basedon the set of calibration data and the one or more parameters, aphysiological parameter of the subject.